pwc-archive/papers-with-abstracts huggingface dataset:
Controlling Chat Bot Multi-Document Navigation with the Extended Discourse Trees
Authors: Dmitry Ilvovsky, Alexander Kirillovich, Boris Galitsky
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Information Retrieval, Retrieval

In this paper we learn how to manage a dialogue relying on discourse of its utterances. We define extended discourse trees, introduce means to manipulate with them, and outline scenarios of multi-document navigation to extend the abilities of the interactive information retrieval-based chat bot. We also provide evaluation results of the comparison between conventional search and chat bot enriched with the multi-document navigation.
Cross-lingual Transfer Learning for Semantic Role Labeling in Russian
Authors: Ilseyar Alimova, Elena Tutubalina, Alexander Kirillovich
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Cross-Lingual Transfer, Semantic Role Labeling, Transfer Learning, XLM-R

This work is devoted to semantic role labeling (SRL) task in Russian. We investigate the role of transfer learning strategies between English FrameNet and Russian FrameBank corpora. We perform experiments with embeddings obtained from various types of multilingual language models, including BERT, XLM-R, MUSE, and LASER. For evaluation, we use a Russian FrameBank dataset. As source data for transfer learning, we experimented with the full version of FrameNet and the reduced dataset with a smaller number of semantic roles identical to FrameBank. Evaluation results demonstrate that BERT embeddings show the best transfer capabilities. The model with pretraining on the reduced English SRL data and fine-tuning on the Russian SRL data show macro-averaged F1-measure of 79.8%, which is above our baseline of 78.4%.
Description Logic Based Formal Representation of Adjectives
Authors: Maria Grits
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

The paper introduces a system of rules for description logic based formal representation of adjectives used in both attributive and predicative functions and involved in a variety of syntactic relations. The system was developed to convey the semantics of adjectives by virtue of concept and role constructors implemented in description logic formalisms known as SHOIN(D) and SROIQ(D). The system is intended to be integrated into a large-scale system of formalization rules devised for the development of description logic based definitions of domain terms. The proposed system of rules was tested and evaluated on two sets of syntactic units that contain attributive and predicative adjectives represented in English and Russian languages.
Linguistic vs. encyclopedic knowledge. Classification of MWEs on the base of domain information
Authors: Zara Kancheva, Ivaylo Radev
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Articles

This paper reports on the first steps in the creation of linked data through the mapping of BTB-WordNet and the Bulgarian Wikipedia. The task of expanding the BTB-WordNet with encyclopedic knowledge is done by mapping its synsets to Wikipedia pages with many MWEs found in the articles and subjected to further analysis. We look for a way to filter the Wikipedia MWEs in the effort of selecting the ones most beneficial to the enrichment of BTB-WN.
It Takes Two to Tango – Towards a Multilingual MWE Resource
Authors: Svetlozara Leseva, Verginica Barbu Mititelu, Ivelina Stoyanova
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Vocal Bursts Valence Prediction

Mature wordnets offer the opportunity of digging out interesting linguistic information otherwise not explicitly marked in the network. The focus in this paper is on the ways the results already obtained at two levels, derivation and multiword expressions, may be further employed. The parallel recent development of the two resources under discussion, the Bulgarian and the Romanian wordnets, has enabled interlingual analyses that reveal similarities and differences between the linguistic knowledge encoded in the two wordnets. In this paper we show how the resources developed and the knowledge gained are put together towards devising a linked MWE resource that is informed by layered dictionary representation and corpus annotation and analysis. This work is a proof of concept for the adopted method of compiling a multilingual MWE resource on the basis of information extracted from the Bulgarian, the Romanian and the Princeton wordnet, as well as additional language resources and automatic procedures.
Generating Natural Language Numerals with TeX
Authors: Ivan Derzhanski, Milena Veneva
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

Sometimes one needs to produce a text in which many numbers have to be written out in words. Writing such a text and ensuring it is error-free can be a burden, especially if the author is not fluent in the language. Such may occur when working on a reference grammar, a research paper or presentation, or a problem on number names for a contest in linguistics. A remedy is to prepare the text with TEX and let some parts be generated automatically. The human effort this takes is to compose a grammar that describes the features of the numeral system. This paper discusses how this is done.
A Natural Language for Bulgarian Primary and Secondary Education
Authors: Iglika Nikolova-Stoupak
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

This paper examines the qualities and applicability of a provisional programming language, especially designed for use by beginner-level students in Bulgarian primary and secondary schools. The necessity for such a language is investigated. Then, relevant features are defined, as inspired by various programming languages (notably, languages used in education and characterised with non- English syntax) and by general trends related to the achievement of natural language in software development. A survey is conducted to test young students’ interaction with the language, and the latter’s advantages and limitations are listed and discussed.
Digital Edition of the Life of St. Petka
Authors: Ivan Šimko
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

This paper presents the construction of a digital edition of multiple versions of the hagiography of St. Petka of Tarnovo. Two related versions are uploaded at first: a Church Slavonic print edition and its later damaskini redaction. Both texts are adapted for user-friendly reading with side-by-side facsimiles. Translations and additional data concerning separate tokens and sentences can be shown up by the cursor on fly. Further metadata will be available for search. Annotation has been adapted for the transitionary status of the language of the texts: it allows us to compare similar morphological forms with various functions. The edition has already been published online and can be used for both teaching and studying. The texts have been digitalized as a part of a larger project concerning the development of the Balkan areal features.
A Bilingual Lexicosemantic Network of Bread Based on a Parallel Corpus
Authors: Ivan Derzhanski, Olena Siruk
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

We present an experiment in using a corpus of Bulgarian and Ukrainian parallel texts for the automatised construction of a bilingual lexicosemantic network representing the semantic field of BREAD. We discuss the extraction of the relevant material from the corpus, the production of networks with varying parameters, some issues of the interpretation of these networks, and possible ways of making them more accurate and informative.
A Customizable WordNet Editor
Authors: Andrei-Marius Avram, Verginica Barbu Mititelu
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

This paper presents an open-source wordnet editor that has been developed to ensure further expansion of the Romanian wordnet. It comes with a web interface that offers capabilities in selecting new synsets to be implemented, editing the list of literals and their sense numbers and adding these new synsets to the existing network, by importing from Princeton WordNet (and adjusting, when necessary) all the relations in which the newly created synsets and their literals are involved. The application also comes with an authorization mechanism that ensures control of the new synsets added in novice or lexicographer accounts. Although created to serve the current (more or less specific) needs in the development of the Romanian wordnet, it can be customized to fulfill new requirements from developers, either of the same wordnet or of a different one for which a similar approach is adopted.
Comparison of Genres in Word Sense Disambiguation using Automatically Generated Text Collections
Authors: Angelina Bolshina, Natalia Loukachevitch
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Word Sense Disambiguation

The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create. In our work we describe an approach that is used to create an automatically labelled collection based on the monosemous relatives (related unambiguous entries) for Russian. The main contribution of our work is that we extracted monosemous relatives that can be located at relatively long distances from a target ambiguous word and ranked them according to the similarity measure to the target sense. We evaluated word sense disambiguation models based on a nearest neighbour classification on BERT and ELMo embeddings and two text collections. Our work relies on the Russian wordnet RuWordNet.
Consistency Evaluation towards Enhancing the Conceptual Representation of Verbs in WordNet
Authors: Svetlozara Leseva, Ivelina Stoyanova
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

This paper outlines the process of enhancing the conceptual description of verb synsets in WordNet using FrameNet frames. On the one hand we expand the coverage of the mapping between WordNet and FrameNet, while on the other – we improve the quality of the mapping using a set of consistency checks and verification procedures. The procedures include an automatic identification of potential inconsistencies and imbalanced relations, as well as suggestions for a more precise frame assignment followed by manual validation. We perform an evaluation of the procedures in terms of the quality of the suggestions measured as the potential improvement in precision and coverage, the relevance of the result and the efficiency of the procedure.
On WordNet Semantic Classes: Is the Sum Always Bigger?
Authors: Tsvetana Dimitrova
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

The paper offers an approach to the validation of the data resulted from a previous effort on expansion of WordNet noun semantic classes by mapping them with the semantic types within the Corpus Pattern Analysis (CPA) ontology employed by the framework of the Pattern Dictionary of English Verbs (PDEV). A case study is presented along with a set of conditions to be checked when validating the combined data.
Detector adaptation by maximising agreement between independent data sources
Authors: Ciaran´ O Conaire, Noel E. O’Connor, Alan F. Smeaton
Date: 2007-07-01 00:00:00
Proceeding: Computer Vision and Pattern Recognition 2007 7

Traditional methods for creating classifiers have two
main disadvantages. Firstly, it is time consuming to acquire, or manually annotate, the training collection. Secondly, the data on which the classifier is trained may be
over-generalised or too specific. This paper presents our
investigations into overcoming both of these drawbacks simultaneously, by providing example applications where two
data sources train each other. This removes both the need
for supervised annotation or feedback, and allows rapid
adaptation of the classifier to different data. Two applications are presented: one using thermal infrared and visual
imagery to robustly learn changing skin models, and another using changes in saturation and luminance to learn
shadow appearance parameters.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction
Authors: Hu Wang, Mao Ye, Xiatian Zhu, Shuai Li, Ce Zhu, Xue Li
Date: 2022-07-23 00:00:00
Proceeding: The 31st International Joint Conference On Artificial Intelligence 2022 7
Tasks: Image Generation, Image Reconstruction, Inverse-Tone-Mapping

Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly , we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https://github.com/wanghu178/KUNet.git.
A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors
Authors: Domonkos Varga
Date: 2022-09-07 00:00:00
Proceeding: Sensors 2022 9
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system’s point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
Authors: Dongmin Hyun, Chanyoung Park, Junsu Cho, Hwanjo Yu
Date: 2022-09-14 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift based on each user's sequential consumption history, but do not explicitly consider whether users' interest in items sustains beyond the training time, i.e., interest sustainability. On the other hand, the item-centric models consider whether users' general interest sustains after the training time, but it is not personalized. In this work, we propose a recommender system taking advantages of the models in both categories. Our proposed model captures personalized interest sustainability, indicating whether each user's interest in items will sustain beyond the training time or not. We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history. We then propose simple yet effective schemes to augment users' sparse consumption history. Extensive experiments show that the proposed model outperforms 10 baseline models on 11 real-world datasets. The codes are available at https://github.com/dmhyun/PERIS.
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
Authors: Darren Tsai, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall
Date: 2022-09-14 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Domain Adaptation, Multi-target Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large component of this reduction in performance. We address this in our approach, SEE-VCN, by designing a novel viewer-centred surface completion network (VCN) to complete the surfaces of objects of interest within an unsupervised domain adaptation framework, SEE. With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns. By adopting a domain-invariant representation, SEE-VCN can be classed as a multi-target domain adaptation approach where no annotations or re-training is required to obtain 3D detections for new scan patterns. Through extensive experiments, we show that our approach outperforms previous domain adaptation methods in multiple domain adaptation settings. Our code and data are available at https://github.com/darrenjkt/SEE-VCN.
Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers
Authors: Yanyun Wang, Dehui Du, YuanHao Liu
Date: 2022-09-14 00:00:00

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.
Personalized Emotion Detection using IoT and Machine Learning
Authors: Fiona Victoria Stanley Jothiraj, Afra Mashhadi
Date: 2022-09-14 00:00:00
Tasks: Cloud Computing

The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics. This paper presents a non-invasive IoT system that tracks patients' emotions, especially those with autism spectrum disorder. With a few affordable sensors and cloud computing services, the individual's heart rates are monitored and analyzed to study the effects of changes in sweat and heartbeats per minute for different emotions. Under normal resting conditions of the individual, the proposed system could detect the right emotion using machine learning algorithms with a performance of up to 92% accuracy. The result of the proposed approach is comparable with the state-of-the-art solutions in medical IoT.
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
Authors: Yanyun Wang, Dehui Du, Haibo Hu, Zi Liang, YuanHao Liu
Date: 2022-09-14 00:00:00
Tasks: Adversarial Attack, global-optimization, Time Series, Time Series Analysis, Time Series Classification

Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC). However, neural networks (NNs) are vulnerable to adversarial samples, which cause real-life adversarial attacks that undermine the robustness of AI models. To date, most existing attacks target at feed-forward NNs and image recognition tasks, but they cannot perform well on RNN-based TSC. This is due to the cyclical computation of RNN, which prevents direct model differentiation. In addition, the high visual sensitivity of time series to perturbations also poses challenges to local objective optimization of adversarial samples. In this paper, we propose an efficient method called TSFool to craft highly-imperceptible adversarial time series for RNN-based TSC. The core idea is a new global optimization objective known as "Camouflage Coefficient" that captures the imperceptibility of adversarial samples from the class distribution. Based on this, we reduce the adversarial attack problem to a multi-objective optimization problem that enhances the perturbation quality. Furthermore, to speed up the optimization process, we propose to use a representation model for RNN to capture deeply embedded vulnerable samples whose features deviate from the latent manifold. Experiments on 11 UCR and UEA datasets showcase that TSFool significantly outperforms six white-box and three black-box benchmark attacks in terms of effectiveness, efficiency and imperceptibility from various perspectives including standard measure, human study and real-world defense.
ConvNeXt Based Neural Network for Audio Anti-Spoofing
Authors: Qiaowei Ma, Jinghui Zhong, Yitao Yang, Weiheng Liu, Ying Gao, Wing W. Y. Ng
Date: 2022-09-14 00:00:00
Tasks: image-classification, Image Classification, Speaker Verification, Speech Synthesis, Synthetic Speech Detection

With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed a number of anti-spoofing methods based on hand-crafted features. However, using hand-crafted features rather than raw waveform will lose implicit information for anti-spoofing. Inspired by the promising performance of ConvNeXt in image classification tasks, we revise the ConvNeXt network architecture and propose a lightweight end-to-end anti-spoofing model. By integrating with the channel attention block and using the focal loss function, the proposed model can focus on the most informative sub-bands of speech representations and the difficult samples that are hard to classify. Experiments show that our proposed system could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the ASVSpoof 2019 LA evaluation dataset, which outperforms the state-of-the-art systems.