pwc-archive/papers-with-abstracts huggingface dataset:
Capitalization and Punctuation Restoration: a Survey
Authors: Vasile Păiş, Dan Tufiş
Date: 2021-11-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Punctuation Restoration, speech-recognition, Speech Recognition, Survey

Ensuring proper punctuation and letter casing is a key pre-processing step towards applying complex natural language processing algorithms. This is especially significant for textual sources where punctuation and casing are missing, such as the raw output of automatic speech recognition systems. Additionally, short text messages and micro-blogging platforms offer unreliable and often wrong punctuation and casing. This survey offers an overview of both historical and state-of-the-art techniques for restoring punctuation and correcting word casing. Furthermore, current challenges and research directions are highlighted.
Textbook to triples: Creating knowledge graph in the form of triples from AI TextBook
Authors: Aman Kumar, Swathi Dinakaran
Date: 2021-11-20 00:00:00
Tasks: Form, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering

A knowledge graph is an essential and trending technology with great applications in entity recognition, search, or question answering. There are a plethora of methods in natural language processing for performing the task of Named entity recognition; however, there are very few methods that could provide triples for a domain-specific text. In this paper, an effort has been made towards developing a system that could convert the text from a given textbook into triples that can be used to visualize as a knowledge graph and use for further applications. The initial assessment and evaluation gave promising results with an F1 score of 82%.
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Authors: Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long
Date: 2021-11-20 00:00:00
Tasks: Answer Selection, Graph Question Answering, Information Retrieval, Learning-To-Rank, Question Answering, Retrieval

Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching model is proposed to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach.
Data Processing Matters: SRPH-Konvergen AI's Machine Translation System for WMT'21
Authors: Lintang Sutawika, Jan Christian Blaise Cruz
Date: 2021-11-20 00:00:00
Tasks: Machine Translation, Translation

In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT'21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest's hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques.
Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution
Authors: Wenpeng Yin, Shelby Heinecke, Jia Li, Nitish Shirish Keskar, Michael Jones, Shouzhong Shi, Stanislav Georgiev, Kurt Milich, Joseph Esposito, Caiming Xiong
Date: 2021-11-20 00:00:00
Tasks: Entity Resolution

The distribution gap between training datasets and data encountered in production is well acknowledged. Training datasets are often constructed over a fixed period of time and by carefully curating the data to be labeled. Thus, training datasets may not contain all possible variations of data that could be encountered in real-world production environments. Tasked with building an entity resolution system - a model that identifies and consolidates data points that represent the same person - our first model exhibited a clear training-production performance gap. In this case study, we discuss our human-in-the-loop enabled, data-centric solution to closing the training-production performance divergence. We conclude with takeaways that apply to data-centric learning at large.
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse
Authors: Ritesh Kumar, Enakshi Nandi, Laishram Niranjana Devi, Shyam Ratan, Siddharth Singh, Akash Bhagat, Yogesh Dawer
Date: 2021-11-19 00:00:00
Proceeding: LREC 2022 6
Tasks: Aggression Identification

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here (and made available as part of the ComMA@ICON shared task), consists of a total 15,000 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English. The paper gives a detailed description of the tagset being used for annotation and also the process of developing a multi-label, fine-grained tagset that can be used for marking comments with aggression and bias of various kinds including gender bias, religious intolerance (called communal bias in the tagset), class/caste bias and ethnic/racial bias. We also define and discuss the tags that have been used for marking different the discursive role being performed through the comments, such as attack, defend, etc. We also present a statistical analysis of the dataset as well as results of our baseline experiments with developing an automatic aggression identification system using the dataset developed.
Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning
Authors: Aron Gohr
Date: 2019-08-01 00:00:00
Proceeding: Conference 2019 8
Tasks: Bayesian Optimization, Cryptanalysis, Deep Learning

This paper has four main contributions.1 First, we calculate the predicted difference distribution of Speck32/64 with one specific input difference under the Markov assumption completely for up to eight rounds and verify that this yields a globally fairly good model of the difference distribution of Speck32/64. Secondly, we show that contrary to conventional wisdom, machine learning can produce very powerful cryptographic distinguishers: for instance, in a simple low-data, chosen plaintext attack on nine rounds of Speck, we present distinguishers based on deep residual neural networks that achieve a mean key rank roughly five times lower than an analogous classical distinguisher using the full difference distribution table. Thirdly, we develop a highly selective key
search policy based on a variant of Bayesian optimization which, together with our neural distinguishers, can be used to reduce the remaining security of 11-round Speck32/64 to roughly 38 bits. This is a significant improvement over previous literature. Lastly, we show that our neural distinguishers successfully use features of the ciphertext pair distribution that are invisible to all purely differential distinguishers even given unlimited data.
While our attack is based on a known input difference taken from the literature, we also show that neural networks can be used to rapidly (within a matter of minutes on our machine) find good input differences without using prior human cryptanalysis.
Dual-Objective Fine-Tuning of BERT for Entity Matching
Authors: Ralph Peeters, Christian Bizer
Date: 2021-06-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2021 6
Tasks: Data Integration, Entity Resolution, Multi-class Classification

An increasing number of data providers have adopted shared numbering schemes such as GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This means for data integration that shared identifiers are often available for a subset of the entity descriptions to be integrated while such identifiers are not available for others. The challenge in these settings is to learn a matcher for entity descriptions without identifiers using the entity descriptions containing identifiers as training data. The task can be approached by learning a binary classifier which distinguishes pairs of entity descriptions for the same real-world entity from descriptions of different entities. The task can also be modeled as a multi-class classification problem by learning classifiers for identifying descriptions of individual entities. We present a dual-objective training method for BERT, called JointBERT, which combines binary matching and multi-class classification, forcing the model to predict the entity identifier for each entity description in a training pair in addition to the match/non-match decision. Our evaluation across five entity matching benchmark datasets shows that dual-objective training can increase the matching performance for seen products by 1% to 5% F1 compared to single-objective Transformer-based methods, given that enough training data is available for both objectives. In order to gain a deeper understanding of the strengths and weaknesses of the proposed method, we compare JointBERT to several other BERT-based matching methods as well as baseline systems along a set of specific matching challenges. This evaluation shows that JointBERT, given enough training data for both objectives, outperforms the other methods on tasks involving seen products, while it underperforms for unseen products. Using a combination of LIME explanations and domain-specific word classes, we analyze the matching decisions of the different deep learning models and conclude that BERT-based models are better at focusing on relevant word classes compared to RNN-based models.
Anti-Jamming Games in Multi-Band Wireless Ad Hoc Networks
Authors: Hyeon-Seong Im, Si-Hyeon Lee
Date: 2021-11-11 00:00:00
Tasks: Collision Avoidance

For multi-band wireless ad hoc networks of multiple users, an anti-jamming game between the users and a jammer is studied. In this game, the users (resp. jammer) want to maximize (resp. minimize) the expected rewards of the users taking into account various factors such as communication rate, hopping cost, and jamming loss. We analyze the arms race of the game and derive an optimal frequency hopping policy at each stage of the arms race based on the Markov decision process (MDP). It is analytically shown that the arms race reaches an equilibrium after a few rounds, and a frequency hopping policy and a jamming strategy at the equilibrium are characterized. We propose two kinds of collision avoidance protocols to ensure that at most one user communicates in each frequency band, and provide various numerical results that show the effects of the reward parameters and collision avoidance protocols on the optimal frequency hopping policy and the expected rewards at the equilibrium. Moreover, we discuss about equilibria for the case where the jammer adopts some unpredictable jamming strategies.
Location-aware Beamforming for MIMO-enabled UAV Communications: An Unknown Input Observer Approach
Authors: Alireza Mohammadi, Mehdi Rahmati, Hafiz Malik
Date: 2021-11-20 00:00:00

Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, a novel spatial estimation for beamforming is proposed to address UAV-based joint sensing and communications (JSC). The proposed spatial estimation algorithm relies on using a delay tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results.
An End-to-End Framework for Dynamic Crime Profiling of Places
Authors: Shailendra Kumar Gupta, Shreyanshu Shekhar, Neeraj Goel, Mukesh Saini
Date: 2021-11-20 00:00:00
Tasks: Articles

Much effort is being made to ensure the safety of people. One of the main requirements of travellers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime location, it needs the past crime history at that location. Such data is not easily available in the public domain, however, it floats around on the Internet in the form of newspaper and social media posts, in an unstructured manner though. Consequently, a large number of works are reported on extracting crime information from news articles, providing piecemeal solutions to the problem. This chapter complements these works by building an end-to-end framework for crime profiling of any given location/area. It customizes individual components of the framework and provides a Spatio-temporal integration of crime information. It develops an automated framework that crawls online news articles, analyzes them, and extracts relevant information to create a crime knowledge base that gets dynamically updated in real-time. The crime density can be easily visualized in the form of a heat map which is generated by the knowledge base. As a case study, it investigates 345448 news articles published by 6 daily English newspapers collected for approximately two years. Experimental results show that the crime profiling matches with the ratings calculated manually by various organizations.
Sound Field Reproduction With Weighted Mode Matching and Infinite-Dimensional Harmonic Analysis: An Experimental Evaluation
Authors: Shoichi Koyama, Keisuke Kimura, Natsuki Ueno
Date: 2021-11-22 00:00:00

Sound field reproduction methods based on numerical optimization, which aim to minimize the error between synthesized and desired sound fields, are useful in many practical scenarios because of their flexibility in the array geometry of loudspeakers. However, the reproduction performance of these methods in a practical environment has not been sufficiently investigated. We evaluate weighted mode matching, which is a sound field reproduction method based on the spherical wavefunction expansion of the sound field, in comparison with conventional pressure matching. We also introduce a method of infinite-dimensional harmonic analysis for estimating the expansion coefficients of the sound field from microphone measurements. Experimental results indicated that weighted mode matching using the expansion coefficients of the transfer functions estimated by the infinite-dimensional harmonic analysis outperforms conventional pressure matching, especially when the number of microphones is small.
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
Authors: Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Hiroshi Sawada, Naoyuki Kamo, Shoko Araki
Date: 2021-11-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), blind source separation, Denoising, speech-recognition, Speech Recognition

This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones. It has been empirically confirmed that Independent Vector Analysis (IVA) can blindly separate N sources from their sound mixture even with diffuse noise when a sufficiently large number (=M) of microphones are available (i.e., M>>N). However, the estimation accuracy seriously degrades as the number of microphones, or more specifically M-N (>=0), decreases. To overcome this limitation of IVA, we propose switching IVA (swIVA) in this paper. With swIVA, time frames of an observed signal with time-varying characteristics are clustered into several groups, each of which can be well handled by IVA using a small number of microphones, and thus accurate estimation can be achieved by applying IVA individually to each of the groups. Conventionally, a switching mechanism was introduced into a beamformer; however, no blind source separation algorithms with a switching mechanism have been successfully developed until this paper. In order to incorporate dereverberation capability, this paper further extends swIVA to blind Convolutional beamforming algorithm (swCIVA). It integrates swIVA and switching Weighted Prediction Error-based dereverberation (swWPE) in a jointly optimal way. We show that both swIVA and swCIVA can be optimized effectively based on blind signal processing, and that their performance can be further improved using a spatial guide for the initialization. Experiments show that both proposed methods largely outperform conventional IVA and its Convolutional beamforming extension (CIVA) in terms of objective signal quality and automatic speech recognition scores when using a relatively small number of microphones.
CDMA for Underwater Acoustic Communication
Authors: Lokesh Bommisetty, Samskruthi Gaddam, Nomula Prakash Reddy, Shaik Basharath, Bharath Are
Date: 2021-11-20 00:00:00

This paper provides an overview of how and why acoustic waves are important for underwater communication when compared with radio and optical waves, underwater acoustic channel, it's characteristics and model. Also about the underwater sensor architecture, challenges faced by sensor nodes in the network and Media Acess Control(MAC) protocol solutions for them. This paper also describes about the various communication approaches including channel coding in underwater and their performance analysis for different approaches used. Through these approaches, multiple underwater vehicles can communicate among themselves as well as with the console with minimal Bit Error Rate.
Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models
Authors: Mattias Luber, Anton Thielmann, Christoph Weisser, Benjamin Säfken
Date: 2021-11-17 00:00:00
Tasks: Community Detection, Topic Models

Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art. However, especially when it comes to short text documents like Tweets, these approaches often lead to unsatisfying results due to the sparsity of the document-feature matrices. Even though, several approaches have been proposed to overcome this sparsity by taking additional information into account, these are merely focused on the aggregation of similar documents and the estimation of word-co-occurrences. This ultimately completely neglects the fact that a lot of topical-information can be actually retrieved from so-called hashtag-graphs by applying common community detection algorithms. Therefore, this paper outlines a novel approach on how to integrate topic structures of hashtag graphs into the estimation of topic models by connecting graph-based community detection and semi-supervised NMF. By applying this approach on recently streamed Twitter data it will be seen that this procedure actually leads to more intuitive and humanly interpretable topics.
Impact of Synchronous Condensers on Power System Static Voltage Stability Considering Line Contingencies in the Presence of Renewable Generation
Authors: Umair Shahzad
Date: 2021-11-22 00:00:00

Ever-growing electrical loads are having a huge impact on the operation and stability of the power system. Moreover, the integration of renewable generation poses various challenges to the future power system, especially, regarding stability. Thus, this paper presents the impact of synchronous condensers (SCs) on static voltage stability analysis for a test transmission network, considering line contingencies, in the presence of renewable generation. The main purpose of this study was to identify critical buses in the power system when line contingencies occur. Both (N-1) and (N-2) contingencies were considered in this study. The impact of renewable generation is also assessed. To analyze the static voltage stability, the conventional power-voltage (P-V) curve method, using continuation power flow (CPF), is applied on the IEEE 14-bus test system. DIgSILENT PowerFactory software simulations are used to obtain the results. The P-V analysis accurately quantifies the critical buses for both cases, considering (N-1) and (N-2) line contingencies.
Measurement Based Non-Line-Of-Sight Vehicular Visible Light Communication Channel Characterization
Authors: Bugra Turan, Omer Narmanlioglu, Osman Nuri Koc, Emrah Kar, Sinem Coleri, Murat Uysal
Date: 2021-11-22 00:00:00

Vehicular visible light communication (V-VLC) aims to provide secure complementary vehicle to everything communications (V2X) to increase road safety and traffic efficiency. V-VLC provides directional transmissions, mainly enabling line-of-sight (LoS) communications. However, reflections due to nearby objects enable non-line-of-sight (NLoS) transmissions, extending the usage scenarios beyond LoS. In this paper, we propose a wide-band measurement-based NLoS channel characterization and evaluate the performance of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) V-VLC scheme for NLoS channel. We propose a distance-based NLoS V-VLC channel path loss model considering reflection surface characteristics and NLoS V-VLC channel impulse response (CIR) incorporating the temporal broadening effect due to vehicle reflections through weighted double gamma function. The proposed path loss model yields higher accuracy up to 14 dB when compared to the single order reflection model whereas the CIR model estimates the full width at half maximum up to 2 ns accuracy. We further demonstrate that the target bit-error-rate of 10^-3 can be achieved up to 7.86 m, 9.79 m, and 17.62 m distances for black, orange, and white vehicle reflection induced measured NLoS V-VLC channels for DCO-OFDM transmissions.