pwc-archive/papers-with-abstracts huggingface dataset:
GenderedNews: Une approche computationnelle des écarts de représentation des genres dans la presse française
Authors: Ange Richard, Gilles Bastin, François Portet
Date: 2022-02-11 00:00:00

In this article, we present {\it GenderedNews} (\url{https://gendered-news.imag.fr}), an online dashboard which gives weekly measures of gender imbalance in French online press. We use Natural Language Processing (NLP) methods to quantify gender inequalities in the media, in the wake of global projects like the Global Media Monitoring Project. Such projects are instrumental in highlighting gender imbalance in the media and its very slow evolution. However, their generalisation is limited by their sampling and cost in terms of time, data and staff. Automation allows us to offer complementary measures to quantify inequalities in gender representation. We understand representation as the presence and distribution of men and women mentioned and quoted in the news -- as opposed to representation as stereotypification. In this paper, we first review different means adopted by previous studies on gender inequality in the media : qualitative content analysis, quantitative content analysis and computational methods. We then detail the methods adopted by {\it GenderedNews} and the two metrics implemented: the masculinity rate of mentions and the proportion of men quoted in online news. We describe the data collected daily (seven main titles of French online news media) and the methodology behind our metrics, as well as a few visualisations. We finally propose to illustrate possible analysis of our data by conducting an in-depth observation of a sample of two months of our database.
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention
Authors: Shahrukh Khan, Mahnoor Shahid
Date: 2022-02-11 00:00:00
Tasks: Sentiment Analysis, Transfer Learning, Word Embeddings

Sentiment Analysis typically refers to using natural language processing, text analysis and computational linguistics to extract affect and emotion based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. We start by training Word2Vec word embeddings for Hindi \textbf{HASOC dataset} and Bengali hate speech and then train LSTM and subsequently, employ parameter sharing based transfer learning to Bengali sentiment classifiers by reusing and fine-tuning the trained weights of Hindi classifiers with both classifier being used as baseline in our study. Finally, we use BiLSTM with self attention in joint dual input learning setting where we train a single neural network on Hindi and Bengali dataset simultaneously using their respective embeddings.
Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass
Authors: Olabanji Shonibare, Xiaosu Tong, Venkatesh Ravichandran
Date: 2022-02-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

It is estimated that around 70 million people worldwide are affected by a speech disorder called stuttering. With recent advances in Automatic Speech Recognition (ASR), voice assistants are increasingly useful in our everyday lives. Many technologies in education, retail, telecommunication and healthcare can now be operated through voice. Unfortunately, these benefits are not accessible for People Who Stutter (PWS). We propose a simple but effective method called 'Detect and Pass' to make modern ASR systems accessible for People Who Stutter in a limited data setting. The algorithm uses a context aware classifier trained on a limited amount of data, to detect acoustic frames that contain stutter. To improve robustness on stuttered speech, this extra information is passed on to the ASR model to be utilized during inference. Our experiments show a reduction of 12.18% to 71.24% in Word Error Rate (WER) across various state of the art ASR systems. Upon varying the threshold of the associated posterior probability of stutter for each stacked frame used in determining low frame rate (LFR) acoustic features, we were able to determine an optimal setting that reduced the WER by 23.93% to 71.67% across different ASR systems.
Fitting Sparse Markov Models to Categorical Time Series Using Regularization
Authors: Tuhin Majumder, Soumendra Lahiri, Donald Martin
Date: 2022-02-11 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

The major problem of fitting a higher order Markov model is the exponentially growing number of parameters. The most popular approach is to use a Variable Length Markov Chain (VLMC), which determines relevant contexts (recent pasts) of variable orders and form a context tree. A more general approach is called Sparse Markov Model (SMM), where all possible histories of order $m$ form a partition so that the transition probability vectors are identical for the histories belonging to a particular group. We develop an elegant method of fitting SMM using convex clustering, which involves regularization. The regularization parameter is selected using BIC criterion. Theoretical results demonstrate the model selection consistency of our method for large sample size. Extensive simulation studies under different set-up have been presented to measure the performance of our method. We apply this method to classify genome sequences, obtained from individuals affected by different viruses.
NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation
Authors: Luyi Ma, Jianpeng Xu, Jason H. D. Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Date: 2022-02-11 00:00:00
Tasks: Recommendation Systems

The complementary item recommender system (CIRS) recommends the complementary items for a given query item. Existing CIRS models consider the item co-purchase signal as a proxy of the complementary relationship due to the lack of human-curated labels from the huge transaction records. These methods represent items in a complementary embedding space and model the complementary relationship as a point estimation of the similarity between items vectors. However, co-purchased items are not necessarily complementary to each other. For example, customers may frequently purchase bananas and bottled water within the same transaction, but these two items are not complementary. Hence, using co-purchase signals directly as labels will aggravate the model performance. On the other hand, the model evaluation will not be trustworthy if the labels for evaluation are not reflecting the true complementary relatedness. To address the above challenges from noisy labeling of the copurchase data, we model the co-purchases of two items as a Gaussian distribution, where the mean denotes the co-purchases from the complementary relatedness, and covariance denotes the co-purchases from the noise. To do so, we represent each item as a Gaussian embedding and parameterize the Gaussian distribution of co-purchases by the means and covariances from item Gaussian embedding. To reduce the impact of the noisy labels during evaluation, we propose an independence test-based method to generate a trustworthy label set with certain confidence. Our extensive experiments on both the publicly available dataset and the large-scale real-world dataset justify the effectiveness of our proposed model in complementary item recommendations compared with the state-of-the-art models.
A Multi-task Learning Framework for Product Ranking with BERT
Authors: Xuyang Wu, Alessandro Magnani, Suthee Chaidaroon, Ajit Puthenputhussery, Ciya Liao, Yi Fang
Date: 2022-02-10 00:00:00
Tasks: Information Retrieval, Multi-Task Learning, Retrieval

Product ranking is a crucial component for many e-commerce services. One of the major challenges in product search is the vocabulary mismatch between query and products, which may be a larger vocabulary gap problem compared to other information retrieval domains. While there is a growing collection of neural learning to match methods aimed specifically at overcoming this issue, they do not leverage the recent advances of large language models for product search. On the other hand, product ranking often deals with multiple types of engagement signals such as clicks, add-to-cart, and purchases, while most of the existing works are focused on optimizing one single metric such as click-through rate, which may suffer from data sparsity. In this work, we propose a novel end-to-end multi-task learning framework for product ranking with BERT to address the above challenges. The proposed model utilizes domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs multi-task learning to optimize multiple objectives simultaneously, which yields a general end-to-end learning framework for product search. We conduct a set of comprehensive experiments on a real-world e-commerce dataset and demonstrate significant improvement of the proposed approach over the state-of-the-art baseline methods.
Information Design for Differential Privacy
Authors: Ian M. Schmutte, Nathan Yoder
Date: 2022-02-11 00:00:00

Firms and statistical agencies must protect the privacy of the individuals whose data they collect, analyze, and publish. Increasingly, these organizations do so by using publication mechanisms that satisfy differential privacy. We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users. We show that mechanisms which add noise to the statistic of interest--like most of those used in practice--are generally not optimal when the statistic is a sum or average of magnitude data (e.g., income). However, we also show that adding noise is always optimal when the statistic is a count of data entries with a certain characteristic, and the underlying database is drawn from a symmetric distribution (e.g., if individuals' data are i.i.d.). When, in addition, data users have supermodular payoffs, we show that the simple geometric mechanism is always optimal by using a novel comparative static that ranks information structures according to their usefulness in supermodular decision problems.
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position
Authors: Hai Cao Manh, Huong Le Thanh, Tuan Luu Minh
Date: 2019-12-04 00:00:00
Proceeding: The Tenth International Symposium 2019 12
Tasks: Clustering, Document Summarization, Extractive Text Summarization, Multi-Document Summarization, Position, Sentence

Multi-document summarization is more challenging than single-document summarization since it has to solve the problem of overlapping information among sentences from different documents. Also, since multi-document summarization dataset is rare, methods based on deep learning are difficult to be applied. In this paper, we propose an approach to multi-document summarization based on a k-means clustering algorithm, combining with the centroid-based method, maximal marginal relevance, and sentence positions. This approach is efficient in finding salient sentences and preventing overlapping between sentences. Experiments using DUC 2007 dataset show that our system is more efficient than other researchers in this field.
Axiomatizing consciousness, with applications
Authors: Henk Barendregt, Antonino Raffone
Date: 2022-01-23 00:00:00

Consciousness will be introduced axiomatically, inspired by Buddhist insight meditation and psychology, logic in computer science, and cognitive neuroscience, as consisting of a stream of $configurations$ that is $compound$, $discrete$, and (non-deterministically) $computable$. Within this context the notions of self, concentration, mindfulness, and various forms of suffering can be defined. As an application of this set up, it will be shown how a combined development of concentration and mindfulness can attenuate and eventually eradicate some of the forms of suffering.
Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding
Authors: Hewei Liu, Shuyuan Zhu, Ruiqin Xiong, Guanghui Liu, Bing Zeng
Date: 2022-01-14 00:00:00

In this paper, we propose a new fast CU partition algorithm for VVC intra coding based on cross-block difference. This difference is measured by the gradient and the content of sub-blocks obtained from partition and is employed to guide the skipping of unnecessary horizontal and vertical partition modes. With this guidance, a fast determination of block partitions is accordingly achieved. Compared with VVC, our proposed method can save 41.64% (on average) encoding time with only 0.97% (on average) increase of BD-rate.
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks
Authors: Peiying Zhang, Fanglin Liu, Chunxiao Jiang, Abderrahim Benslimane, Juan-Luis Gorricho, Joan Serrat-Fernacute
Date: 2022-02-07 00:00:00
Tasks: Blocking, Network Embedding

Virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a lot of research has focused on the evolutionary algorithm's masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.
Nonprehensile Manipulation of a Stick Using Impulsive Forces
Authors: Aakash Khandelwal, Nilay Kant, Ranjan Mukherjee
Date: 2022-02-11 00:00:00

The problem of nonprehensile manipulation of a stick in three-dimensional space using intermittent impulsive forces is considered. The objective is to juggle the stick between a sequence of configurations that are rotationally symmetric about the vertical axis. The dynamics of the stick is described by five generalized coordinates and three control inputs. Between two consecutive configurations where impulsive inputs are applied, the dynamics is conveniently represented by a Poincar\'e map in the reference frame of the juggler. Stabilization of the orbit associated with a desired juggling motion is accomplished by stabilizing a fixed point on the Poincar\'e map. The Impulse Controlled Poincar\'e Map approach is used to stabilize the orbit, and numerical simulations are used to demonstrate convergence to the desired juggling motion from an arbitrary initial configuration. In the limiting case, where consecutive rotationally symmetric configurations are chosen arbitrarily close, it is shown that the dynamics reduces to that of steady precession of the stick on a hoop.
The xmuspeech system for multi-channel multi-party meeting transcription challenge
Authors: Jie Wang, Yuji Liu, Binling Wang, Yiming Zhi, Song Li1, Shipeng Xia, Jiayang Zhang, Lin Li1, Qingyang Hong, Feng Tong
Date: 2022-02-11 00:00:00
Tasks: speaker-diarization, Speaker Diarization

This paper describes the system developed by the XMUSPEECH team for the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). For the speaker diarization task, we propose a multi-channel speaker diarization system that obtains spatial information of speaker by Difference of Arrival (DOA) technology. Speaker-spatial embedding is generated by x-vector and s-vector derived from Filter-and-Sum Beamforming (FSB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named Discriminative Multi-stream Neural Network (DMSNet) which consists of Attention Filter-and-Sum block (AFSB) and Conformer encoder. We explore DMSNet to address overlapped speech problem on multi-channel audio. Compared with LSTM based OSD module, we achieve a decreases of 10.1% in Detection Error Rate(DetER). By performing DMSNet based OSD module, the DER of cluster-based diarization system decrease significantly form 13.44% to 7.63%. Our best fusion system achieves 7.09% and 9.80% of the diarization error rate (DER) on evaluation set and test set.
Neural Architecture Search for Energy Efficient Always-on Audio Models
Authors: Daniel T. Speckhard, Karolis Misiunas, Sagi Perel, Tenghui Zhu, Simon Carlile, Malcolm Slaney
Date: 2022-02-09 00:00:00
Tasks: Audio Classification, Classification, Edge-computing, Efficient Neural Network, Neural Architecture Search

Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural architecture searches (NAS) that improve the chance of success in practical situations. Our search simultaneously optimizes for network accuracy, energy efficiency and memory usage. We benchmark the performance of our search on real hardware, but since running thousands of tests with real hardware is difficult we use a random forest model to roughly predict the energy usage of a candidate network. We present a search strategy that uses both Bayesian and regularized evolutionary search with particle swarms, and employs early-stopping to reduce the computational burden. Our search, evaluated on a sound-event classification dataset based upon AudioSet, results in an order of magnitude less energy per inference and a much smaller memory footprint than our baseline MobileNetV1/V2 implementations while slightly improving task accuracy. We also demonstrate how combining a 2D spectrogram with a convolution with many filters causes a computational bottleneck for audio classification and that alternative approaches reduce the computational burden but sacrifice task accuracy.
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM
Authors: Haleh Tabrizi, Borja Peleato, Golnaz Farhadi, John M. Cioffi, Ghadah Aldabbagh
Date: 2022-02-11 00:00:00
Tasks: Distributed Optimization

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{distance from the destination} and creates a structured multi-hop configuration in which each group can relay its neighbor's data. \newt{The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups}. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. \newt{The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain} a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.
Wind power ramp prediction algorithm based on wavelet deep belief network
Authors: Zhenhao Tang, Qingyu Meng, Shengxian Cao, Yang Li, Zhongha Mu, Xiaoya Pang
Date: 2022-02-11 00:00:00
Tasks: feature selection, Prediction, Time Series, Time Series Analysis

The wind power ramp events threaten the power grid safety significantly. To improve the ramp prediction accuracy, a hybrid wavelet deep belief network algorithm with adaptive feature selection (WDBNAFS) is proposed. First, the wind power characteristic is analyzed. Then, wavelet decomposition is addressed to the time series, and an adaptive feature selection algorithm is proposed to select the inputs of the prediction model. Finally, a deep belief network is employed to predict the wind power ramp event, and the proposed WDBNAFS was testified with the experiments based on the practical data. The simulation results demonstrate that the prediction accuracy of the proposed algorithm is more than 90%.
Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution
Authors: Johan Kon, Marcel Heertjes, Tom Oomen
Date: 2022-02-10 00:00:00

An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degrades control performance, and to provide a solution. The proposed method employs instrumental variables to ensure consistent parameter estimates. A nonlinear system example reveals that the developed instrumental variable neural network (IVNN) approach asymptotically recovers the optimal solution, while pre-existing approaches are shown to lead to inconsistent estimates.
A constraint on the dynamics of wealth concentration
Authors: Valerio Astuti
Date: 2022-02-11 00:00:00

In the context of a large class of stochastic processes used to describe the dynamics of wealth growth, we prove a set of inequalities establishing necessary and sufficient conditions in order to avoid infinite wealth concentration. These inequalities generalize results previously found only in the context of particular models, or with more restrictive sets of hypotheses. In particular, we emphasize the role of the additive component of growth - usually representing labor incomes - in limiting the growth of inequality. Our main result is a proof that in an economy with random wealth growth, with returns non-negatively correlated with wealth, an average labor income growing at least proportionally to the average wealth is necessary to avoid a runaway concentration. One of the main advantages of this result with respect to the standard economics literature is the independence from the concept of an equilibrium wealth distribution, which does not always exist in random growth models. We analyze in this light three toy models, widely studied in the economics and econophysics literature.