pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative construction of lexicographic and parallel datasets for African languages: first assessment
Authors: Elvis Mboning Tchiaze
Date: 2021-03-30 00:00:00
Tasks: Natural Language Understanding

Faced with a considerable lack of resources in African languages to carry out work in Natural Language Processing (NLP), Natural Language Understanding (NLU) and artificial intelligence, the research teams of NTeALan association has set itself the objective of building open-source platforms for the collaborative construction of lexicographic data in African languages. In this article, we present our first reports after 2 years of collaborative construction of lexicographic resources useful for African NLP tools.
Pre-training for low resource speech-to-intent applications
Authors: Pu Wang, Hugo Van hamme
Date: 2021-03-30 00:00:00
Tasks: Decoder

Designing a speech-to-intent (S2I) agent which maps the users' spoken commands to the agents' desired task actions can be challenging due to the diverse grammatical and lexical preference of different users. As a remedy, we discuss a user-taught S2I system in this paper. The user-taught system learns from scratch from the users' spoken input with action demonstration, which ensure it is fully matched to the users' way of formulating intents and their articulation habits. The main issue is the scarce training data due to the user effort involved. Existing state-of-art approaches in this setting are based on non-negative matrix factorization (NMF) and capsule networks. In this paper we combine the encoder of an end-to-end ASR system with the prior NMF/capsule network-based user-taught decoder, and investigate whether pre-training methodology can reduce training data requirements for the NMF and capsule network. Experimental results show the pre-trained ASR-NMF framework significantly outperforms other models, and also, we discuss limitations of pre-training with different types of command-and-control(C&C) applications.
Evaluating the Morphosyntactic Well-formedness of Generated Texts
Authors: Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
Date: 2021-03-30 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Machine Translation, Text Generation, Translation

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
XY Neural Networks
Authors: Nikita Stroev, Natalia G. Berloff
Date: 2021-03-31 00:00:00
Tasks: speech-recognition, Speech Recognition

The classical XY model is a lattice model of statistical mechanics notable for its universality in the rich hierarchy of the optical, laser and condensed matter systems. We show how to build complex structures for machine learning based on the XY model's nonlinear blocks. The final target is to reproduce the deep learning architectures, which can perform complicated tasks usually attributed to such architectures: speech recognition, visual processing, or other complex classification types with high quality. We developed the robust and transparent approach for the construction of such models, which has universal applicability (i.e. does not strongly connect to any particular physical system), allows many possible extensions while at the same time preserving the simplicity of the methodology.
A Signal-Centric Perspective on the Evolution of Symbolic Communication
Authors: Quintino Francesco Lotito, Leonardo Lucio Custode, Giovanni Iacca
Date: 2021-03-31 00:00:00

The evolution of symbolic communication is a longstanding open research question in biology. While some theories suggest that it originated from sub-symbolic communication (i.e., iconic or indexical), little experimental evidence exists on how organisms can actually evolve to define a shared set of symbols with unique interpretable meaning, thus being capable of encoding and decoding discrete information. Here, we use a simple synthetic model composed of sender and receiver agents controlled by Continuous-Time Recurrent Neural Networks, which are optimized by means of neuro-evolution. We characterize signal decoding as either regression or classification, with limited and unlimited signal amplitude. First, we show how this choice affects the complexity of the evolutionary search, and leads to different levels of generalization. We then assess the effect of noise, and test the evolved signaling system in a referential game. In various settings, we observe agents evolving to share a dictionary of symbols, with each symbol spontaneously associated to a 1-D unique signal. Finally, we analyze the constellation of signals associated to the evolved signaling systems and note that in most cases these resemble a Pulse Amplitude Modulation system.
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
Authors: Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé
Date: 2021-03-31 00:00:00
Tasks: BIG-bench Machine Learning

We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties. In particular, we compute the feature space dimensions of the resulting polynomial kernels, prove that the reproducing kernel Hilbert spaces induced by symmetric and antisymmetric Gaussian kernels are dense in the space of symmetric and antisymmetric functions, and propose a Slater determinant representation of the antisymmetric Gaussian kernel, which allows for an efficient evaluation even if the state space is high-dimensional. Furthermore, we show that by exploiting symmetries or antisymmetries the size of the training data set can be significantly reduced. The results are illustrated with guiding examples and simple quantum physics and chemistry applications.
Continuous Latent Position Models for Instantaneous Interactions
Authors: Riccardo Rastelli, Marco Corneli
Date: 2021-03-31 00:00:00
Tasks: Position

We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays, and easily available. Examples of instantaneous interactions include email networks, phone call networks and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent position network model: we assume that the entities are embedded in a latent Euclidean space, and that they move along individual trajectories which are continuous over time. These trajectories are used to characterize the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the individual trajectories from the observed interaction data, and propose applications on artificial and real data.
MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19
Authors: Axel Oehmichen, Florian Guitton, Cedric Wahl, Bertrand Foing, Damian Tziamtzis, Yike Guo
Date: 2021-03-31 00:00:00
Tasks: Epidemiology, Privacy Preserving

Epidemiology models play a key role in understanding and responding to the COVID-19 pandemic. In order to build those models, scientists need to understand contributing factors and their relative importance. A large strand of literature has identified the importance of airflow to mitigate droplets and far-field aerosol transmission risks. However, the specific factors contributing to higher or lower contamination in various settings have not been clearly defined and quantified. As part of the MOAI project (https://moaiapp.com), we are developing a privacy-preserving test and trace app to enable infection cluster investigators to get in touch with patients without having to know their identity. This approach allows involving users in the fight against the pandemic by contributing additional information in the form of anonymous research questionnaires. We first describe how the questionnaire was designed, and the synthetic data was generated based on a review we carried out on the latest available literature. We then present a model to evaluate the risk exposition of a user for a given setting. We finally propose a temporal addition to the model to evaluate the risk exposure over time for a given user.
Revisiting Bayesian Optimization in the light of the COCO benchmark
Authors: Rodolphe Le Riche, Victor Picheny
Date: 2021-03-30 00:00:00
Tasks: Bayesian Optimization

It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions. However, BO is not often compared to widely different alternatives, and is mostly tested on narrow sets of problems (multimodal, low-dimensional functions), which makes it difficult to assess where (or if) they actually achieve state-of-the-art performance. Moreover, several aspects in the design of these algorithms vary across implementations without a clear recommendation emerging from current practices, and many of these design choices are not substantiated by authoritative test campaigns. This article reports a large investigation about the effects on the performance of (Gaussian process based) BO of common and less common design choices. The experiments are carried out with the established COCO (COmparing Continuous Optimizers) software. It is found that a small initial budget, a quadratic trend, high-quality optimization of the acquisition criterion bring consistent progress. Using the GP mean as an occasional acquisition contributes to a negligible additional improvement. Warping degrades performance. The Mat\'ern 5/2 kernel is a good default but it may be surpassed by the exponential kernel on irregular functions. Overall, the best EGO variants are competitive or improve over state-of-the-art algorithms in dimensions less or equal to 5 for multimodal functions. The code developed for this study makes the new version (v2.1.1) of the R package DiceOptim available on CRAN. The structure of the experiments by function groups allows to define priorities for future research on Bayesian optimization.
An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
Authors: Koustav Rudra, Zeon Trevor Fernando, Avishek Anand
Date: 2021-03-30 00:00:00
Tasks: Document Ranking, Retrieval

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT. Researchers proposed ranking strategies that either truncate the documents beyond the token limit or chunk the documents into units that can fit into the BERT. In the later case, the relevance labels are either directly transferred from the original query-document pair or learned through some external model. In this paper, we conduct a detailed study of the design decisions about splitting and label transfer on retrieval effectiveness and efficiency. We find that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness for large training datasets. We also find that query processing times are adversely affected by fine-grained splitting schemes. As a remedy, we propose a careful passage level labelling scheme using weak supervision that delivers improved performance (3-14% in terms of nDCG score) over most of the recently proposed models for ad-hoc retrieval while maintaining manageable computational complexity on four diverse document retrieval datasets.
A Systematic Literature Review on Process-Aware Recommender Systems
Authors: Mansoureh Yari Eili, Jalal Rezaeenour, Mohammadreza Fani Sani
Date: 2021-03-30 00:00:00
Tasks: Articles, Descriptive, Management, Recommendation Systems, Systematic Literature Review

Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowledge on process mining potential for recommendation problems. Therefore, this paper aims to identify and analyze the published studies on process-aware recommender system techniques in business process management and process mining domain. A systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects. In this regard, we provide a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field. Moreover, based on a knowledge base and holistic perspective, we discuss some research gaps and open challenges in this field.
Adversarial Attacks and Defenses for Speech Recognition Systems
Authors: Piotr Żelasko, Sonal Joshi, Yiwen Shao, Jesus Villalba, Jan Trmal, Najim Dehak, Sanjeev Khudanpur
Date: 2021-03-31 00:00:00
Tasks: Adversarial Robustness, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

The ubiquitous presence of machine learning systems in our lives necessitates research into their vulnerabilities and appropriate countermeasures. In particular, we investigate the effectiveness of adversarial attacks and defenses against automatic speech recognition (ASR) systems. We select two ASR models - a thoroughly studied DeepSpeech model and a more recent Espresso framework Transformer encoder-decoder model. We investigate two threat models: a denial-of-service scenario where fast gradient-sign method (FGSM) or weak projected gradient descent (PGD) attacks are used to degrade the model's word error rate (WER); and a targeted scenario where a more potent imperceptible attack forces the system to recognize a specific phrase. We find that the attack transferability across the investigated ASR systems is limited. To defend the model, we use two preprocessing defenses: randomized smoothing and WaveGAN-based vocoder, and find that they significantly improve the model's adversarial robustness. We show that a WaveGAN vocoder can be a useful countermeasure to adversarial attacks on ASR systems - even when it is jointly attacked with the ASR, the target phrases' word error rate is high.
“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？科学博士
Authors: 科学博士
Date: 2021-03-31 00:00:00

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
#科学博士​ #苏伊士运河​ #长赐号轮船​ #罪魁祸首​ #原因​

★推荐影片★
直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec​

世上居然有三黄蛋！三黄蛋！99.9%的人一生都没有见过！
https://youtu.be/YvkXjYK0fd4​

稀奇的双黄蛋：万分之一的鸡蛋！双黄蛋密码| 科学博士
https://youtu.be/1rDJq_vUl6Q​

哇！透明的双黄蛋，怎么做的？你在家也可以的！科学博士的家庭实验室
https://youtu.be/rlRWZBZ9sf0
The Division of Assets in Multiagent Systems: A Case Study in Team Blotto Games
Authors: Keith Paarporn, Rahul Chandan, Mahnoosh Alizadeh, Jason R. Marden
Date: 2021-03-30 00:00:00

Multi-agent systems are designed to concurrently accomplish a diverse set of tasks at unprecedented scale. Here, the central problems faced by a system operator are to decide (i) how to divide available resources amongst the agents assigned to tasks and (ii) how to coordinate the behavior of the agents to optimize the efficiency of the resulting collective behavior. The focus of this paper is on problem (i), where we seek to characterize the impact of the division of resources on the best-case efficiency of the resulting collective behavior. Specifically, we focus on a team Colonel Blotto game where there are two sub-colonels competing against a common adversary in a two battlefield environment. Here, each sub-colonel is assigned a given resource budget and is required to allocate these resources independent of the other sub-colonel. However, their success is dependent on the allocation strategy of both sub-colonels. The central focus of this manuscript is on how to divide a common pool of resources among the two sub-colonels to optimize the resulting best-case efficiency guarantees. Intuitively, one would imagine that the more balanced the division of resources, the worse the performance, as such divisions restrict the sub-colonels' ability to employ joint randomized strategies that tend to be necessary for optimizing performance guarantees. However, the main result of this paper demonstrates that this intuition is actually incorrect. A more balanced division of resources can offer better performance guarantees than a more centralized division. Hence, this paper demonstrates that the resource division problem is highly non-trivial in such enmeshed environments and worthy of significant future research efforts.
真的吗？北大未名湖结冰后人可以在湖面行走？北京大学【字幕】
Authors: 科学博士
Date: 2021-02-26 00:00:00

真的吗？北大未名湖结冰后人可以在湖面行走？
#科学博士​ #北京大学​ #未名湖湖面行走​ #未名湖结冰​
北京大学是中国最高学府，人文气息浓厚，景色优美，作为北京大学最美的地方未名湖，冬天景色也是别具一格，未名湖湖面结冰到底哟多厚，人真的可以在湖面行走吗？不会掉进湖里然后XX？快点击进入揭晓答案同时订阅关注科学博士频道吧！
本视频记得打开CC字幕。

★推荐影片★
好奇，国家总理吃过的北京大学食堂到底长啥样？都有哪些特色菜？超清视频
https://youtu.be/Vf2JfClMges​

北大未名湖畔的小猫咪居然不怕人好萌呀，赏美景撸猫咪，么么哒！超清视频
https://youtu.be/NkdHdWJ9-SI​

走进北京大学图书馆，亚洲最大，知识的圣殿，多少人梦寐以求想在这里看书
https://youtu.be/jErniaRkv9k
State-Dependent Processing in Payment Channel Networks for Throughput Optimization
Authors: Nikolaos Papadis, Leandros Tassiulas
Date: 2021-03-31 00:00:00
Tasks: Scheduling

Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two nodes to safely transact between themselves in high frequencies based on pre-committed peer-to-peer balances. Transaction requests in these networks may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in PCNs: as transactions continuously arrive on both sides of a channel, nodes need to decide which ones to process and when in order to maximize their objective, which in our case is the channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under random arrivals, and propose that channels can hold incoming transactions in buffers up to some deadline in order to enable more elaborate processing decisions. We describe a policy that maximizes the channel success rate/throughput for uniform transaction requests of fixed amounts, both in the presence and absence of buffering capabilities, and formally prove its optimality. We also develop a discrete event simulator of a payment channel, and evaluate different heuristic scheduling policies in the more general heterogeneous amounts case, with the results showing superiority of the heuristic extension of our policy in this case as well. Our work opens the way for more formal research on improving PCN performance via joint consideration of routing and scheduling decisions.
北大未名湖畔的小猫咪居然不怕人好萌呀，赏美景撸猫咪，么么哒！超清视频【字幕】
Authors: 科学博士
Date: 2021-02-15 00:00:00

北大未名湖畔的小猫咪居然不怕人好萌呀，赏美景撸猫咪，么么哒！超清视频
#科学博士​ #北京大学​ #未名湖小猫咪​ #北大撸猫​
北京大学是中国最高学府，人文气息浓厚，景色优美，作为北京大学最美的地方未名湖，坐在湖畔撸小猫咪会是怎样的一种享受呢？快点击进入观看同时订阅关注科学博士频道吧！
本视频记得打开CC字幕。

★推荐影片★
好奇，国家总理吃过的北京大学食堂到底长啥样？都有哪些特色菜？超清视频
https://youtu.be/Vf2JfClMges​

走进北京大学图书馆，亚洲最大，知识的圣殿，多少人梦寐以求想在这里看书
https://youtu.be/jErniaRkv9k​

真的吗？北大未名湖结冰后人可以在湖面行走？北京大学
https://youtu.be/fT9JIlQjJBQ
Y$^2$-Net FCRN for Acoustic Echo and Noise Suppression
Authors: Ernst Seidel, Jan Franzen, Maximilian Strake, Tim Fingscheidt
Date: 2021-03-31 00:00:00
Tasks: Acoustic echo cancellation

In recent years, deep neural networks (DNNs) were studied as an alternative to traditional acoustic echo cancellation (AEC) algorithms. The proposed models achieved remarkable performance for the separate tasks of AEC and residual echo suppression (RES). A promising network topology is a fully convolutional recurrent network (FCRN) structure, which has already proven its performance on both noise suppression and AEC tasks, individually. However, the combination of AEC, postfiltering, and noise suppression to a single network typically leads to a noticeable decline in the quality of the near-end speech component due to the lack of a separate loss for echo estimation. In this paper, we propose a two-stage model (Y$^2$-Net) which consists of two FCRNs, each with two inputs and one output (Y-Net). The first stage (AEC) yields an echo estimate, which - as a novelty for a DNN AEC model - is further used by the second stage to perform RES and noise suppression. While the subjective listening test of the Interspeech 2021 AEC Challenge mostly yielded results close to the baseline, the proposed method scored an average improvement of 0.46 points over the baseline on the blind testset in double-talk on the instrumental metric DECMOS, provided by the challenge organizers.
SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification
Authors: Helin Wang, Yuexian Zou, Wenwu Wang
Date: 2021-03-31 00:00:00
Tasks: Acoustic Scene Classification, Data Augmentation, General Classification, Scene Classification

In this paper, we present SpecAugment++, a novel data augmentation method for deep neural networks based acoustic scene classification (ASC). Different from other popular data augmentation methods such as SpecAugment and mixup that only work on the input space, SpecAugment++ is applied to both the input space and the hidden space of the deep neural networks to enhance the input and the intermediate feature representations. For an intermediate hidden state, the augmentation techniques consist of masking blocks of frequency channels and masking blocks of time frames, which improve generalization by enabling a model to attend not only to the most discriminative parts of the feature, but also the entire parts. Apart from using zeros for masking, we also examine two approaches for masking based on the use of other samples within the minibatch, which helps introduce noises to the networks to make them more discriminative for classification. The experimental results on the DCASE 2018 Task1 dataset and DCASE 2019 Task1 dataset show that our proposed method can obtain 3.6% and 4.7% accuracy gains over a strong baseline without augmentation (i.e. CP-ResNet) respectively, and outperforms other previous data augmentation methods.