pwc-archive/papers-with-abstracts huggingface dataset:
Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation
Authors: Llion Jones, Richard Sproat, Haruko Ishikawa, Alexander Gutkin
Date: 2022-10-18 00:00:00

If one sees the place name Houston Mercer Dog Run in New York, how does one know how to pronounce it? Assuming one knows that Houston in New York is pronounced "how-ston" and not like the Texas city, then one can probably guess that "how-ston" is also used in the name of the dog park. We present a novel architecture that learns to use the pronunciations of neighboring names in order to guess the pronunciation of a given target feature. Applied to Japanese place names, we demonstrate the utility of the model to finding and proposing corrections for errors in Google Maps. To demonstrate the utility of this approach to structurally similar problems, we also report on an application to a totally different task: Cognate reflex prediction in comparative historical linguistics. A version of the code has been open-sourced (https://github.com/google-research/google-research/tree/master/cognate_inpaint_neighbors).
Simple and Effective Unsupervised Speech Translation
Authors: Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
Date: 2022-10-18 00:00:00
Tasks: Domain Adaptation, Machine Translation, speech-recognition, Speech Recognition, Speech Synthesis, Speech-to-Text, Speech-to-Text Translation, Translation, Unsupervised Domain Adaptation, Unsupervised Speech Recognition

The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages. To address this issue, we study a simple and effective approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis, either in a pipeline approach, or to generate pseudo-labels for training end-to-end speech translation models. Furthermore, we present an unsupervised domain adaptation technique for pre-trained speech models which improves the performance of downstream unsupervised speech recognition, especially for low-resource settings. Experiments show that unsupervised speech-to-text translation outperforms the previous unsupervised state of the art by 3.2 BLEU on the Libri-Trans benchmark, on CoVoST 2, our best systems outperform the best supervised end-to-end models (without pre-training) from only two years ago by an average of 5.0 BLEU over five X-En directions. We also report competitive results on MuST-C and CVSS benchmarks.
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
Authors: Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie
Date: 2022-10-18 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, XLM-R

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our systems employ the framework of UniTE, which combined three types of input formats during training with a pre-trained language model. First, we apply the pseudo-labeled data examples for the continuously pre-training phase. Notably, to reduce the gap between pre-training and fine-tuning, we use data pruning and a ranking-based score normalization strategy. For the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions. Finally, we collect the source-only evaluation results, and ensemble the predictions generated by two UniTE models, whose backbones are XLM-R and InfoXLM, respectively. Results show that our models reach 1st overall ranking in the Multilingual and English-Russian settings, and 2nd overall ranking in English-German and Chinese-English settings, showing relatively strong performances in this year's quality estimation competition.
Efficient, probabilistic analysis of combinatorial neural codes
Authors: Thomas F Burns, Irwansyah
Date: 2022-10-19 00:00:00
Tasks: image-classification, Image Classification

Artificial and biological neural networks (ANNs and BNNs) can encode inputs in the form of combinations of individual neurons' activities. These combinatorial neural codes present a computational challenge for direct and efficient analysis due to their high dimensionality and often large volumes of data. Here we improve the computational complexity -- from factorial to quadratic time -- of direct algebraic methods previously applied to small examples and apply them to large neural codes generated by experiments. These methods provide a novel and efficient way of probing algebraic, geometric, and topological characteristics of combinatorial neural codes and provide insights into how such characteristics are related to learning and experience in neural networks. We introduce a procedure to perform hypothesis testing on the intrinsic features of neural codes using information geometry. We then apply these methods to neural activities from an ANN for image classification and a BNN for 2D navigation to, without observing any inputs or outputs, estimate the structure and dimensionality of the stimulus or task space. Additionally, we demonstrate how an ANN varies its internal representations across network depth and during learning.
Application of Decision Tree Classifier in Detection of Specific Denial of Service Attacks with Genetic Algorithm Based Feature Selection on NSL-KDD
Authors: Deanna Wilborne
Date: 2022-10-19 00:00:00
Tasks: Combinatorial Optimization, feature selection

Using a Genetic Algorithm and Decision Tree Classifier, the features of the NSL-KDD dataset are reduced using combinatorial optimization to determine the minimum features required to accurately classify Denial of Service attacks within the NSL-KDD dataset.
A Flexible Approach for Normal Approximation of Geometric and Topological Statistics
Authors: Zhaoyang Shi, Krishnakumar Balasubramanian, Wolfgang Polonik
Date: 2022-10-19 00:00:00

We derive normal approximation results for a class of stabilizing functionals of binomial or Poisson point process, that are not necessarily expressible as sums of certain score functions. Our approach is based on a flexible notion of the add-one cost operator, which helps one to deal with the second-order cost operator via suitably appropriate first-order operators. We combine this flexible notion with the theory of strong stabilization to establish our results. We illustrate the applicability of our results by establishing normal approximation results for certain geometric and topological statistics arising frequently in practice. Several existing results also emerge as special cases of our approach.
Bayesian Emulation for Computer Models with Multiple Partial Discontinuities
Authors: Ian Vernon, Jonathan Owen, Jonathan Carter
Date: 2022-10-19 00:00:00
Tasks: Uncertainty Quantification

Computer models are widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical construct that mimics the slow to evaluate computer model, and greatly aids the vastly more computationally intensive uncertainty quantification calculations that an important scientific analysis often requires. We examine the problem of emulating computer models that possess multiple, partial discontinuities occurring at known non-linear location. We introduce the TENSE framework, based on carefully designed correlation structures that respect the discontinuities while enabling full exploitation of any smoothness/continuity elsewhere. This leads to a single emulator object that can be updated by all runs simultaneously, and also used for efficient design. This approach avoids having to split the input space into multiple subregions. We apply the TENSE framework to the TNO Challenge II, emulating the OLYMPUS reservoir model, which possess multiple such discontinuities.
Distributional Adaptive Soft Regression Trees
Authors: Nikolaus Umlauf, Nadja Klein
Date: 2022-10-19 00:00:00
Tasks: regression

Random forests are an ensemble method relevant for many problems, such as regression or classification. They are popular due to their good predictive performance (compared to, e.g., decision trees) requiring only minimal tuning of hyperparameters. They are built via aggregation of multiple regression trees during training and are usually calculated recursively using hard splitting rules. Recently regression forests have been incorporated into the framework of distributional regression, a nowadays popular regression approach aiming at estimating complete conditional distributions rather than relating the mean of an output variable to input features only - as done classically. This article proposes a new type of a distributional regression tree using a multivariate soft split rule. One great advantage of the soft split is that smooth high-dimensional functions can be estimated with only one tree while the complexity of the function is controlled adaptive by information criteria. Moreover, the search for the optimal split variable is obsolete. We show by means of extensive simulation studies that the algorithm has excellent properties and outperforms various benchmark methods, especially in the presence of complex non-linear feature interactions. Finally, we illustrate the usefulness of our approach with an example on probabilistic forecasts for the Sun's activity.
Multivariate outlier explanations using Shapley values and Mahalanobis distances
Authors: Marcus Mayrhofer, Peter Filzmoser
Date: 2022-10-18 00:00:00
Tasks: Outlier Interpretation, Position

For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions originating from single variables. The decomposition is obtained using the Shapley value, a well-known concept from game theory that became popular in the context of Explainable AI. In addition to outlier explanation, this concept also relates to the recent formulation of cellwise outlyingness, where Shapley values can be employed to obtain variable contributions for outlying observations with respect to their "expected" position given the multivariate data structure. In combination with squared Mahalanobis distances, Shapley values can be calculated at a low numerical cost, making them even more attractive for outlier interpretation. Simulations and real-world data examples demonstrate the usefulness of these concepts.
Data-Augmented Counterfactual Learning for Bundle Recommendation
Authors: Shixuan Zhu, Qi Shen, Yiming Zhang, Zhenwei Dong, Zhihua Wei
Date: 2022-10-19 00:00:00
Tasks: counterfactual, Data Augmentation, Graph Learning, Model Optimization

Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists on a music platform or book lists on a reading website. Several graph based models have achieved state-of-the-art performance on BR task. But their performance is still sub-optimal, since the data sparsity problem tends to be more severe in real bundle recommendation scenarios, which limits graph-based models from more sufficient learning. In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation. Our paradigm consists of two main parts: counterfactual data augmentation and counterfactual constraint. The main idea of our paradigm lies in answering the counterfactual questions: "What would a user interact with if his/her interaction history changes?" "What would a user interact with if the bundle-item affiliation relations change?" In counterfactual data augmentation, we design a heuristic sampler to generate counterfactual graph views for graph-based models, which has better noise controlling than the stochastic sampler. We further propose counterfactual loss to constrain model learning for mitigating the effects of residual noise in augmented data and achieving more sufficient model optimization. Further theoretical analysis demonstrates the rationality of our design. Extensive experiments of BR models applied with our paradigm on two real-world datasets are conducted to verify the effectiveness of the paradigm.
Evaluation Metrics for Measuring Bias in Search Engine Results
Authors: Gizem Gezici, Aldo Lipani, Yucel Saygin, Emine Yilmaz
Date: 2022-10-19 00:00:00

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society.
A Study of Obstacles in Plagiarism Software Subscribing by Colleges in Tamil Nadu
Authors: Subaveerapandiyan A, Sakthivel N
Date: 2022-10-19 00:00:00

This article attempts to comprehend the current issues and hurdles that Indian colleges affiliated with Tamil Nadu State Universities encounter when trying to subscribe to a software that detects plagiarism. The study goals are to determine whether colleges employ anti-plagiarism software, whether they ensure that their student-given assignments are free of copyright infringement, whether tutors teach about academic misconduct, and what people seem to think of anti-plagiarism software. We surveyed for this study and distributed the questionnaires among college administrators, principals, and librarians.
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
Authors: Tetsuya Sakai, Sijie Tao, Maria Maistro, Zhumin Chu, Yujing Li, Nuo Chen, Nicola Ferro, Junjie Wang, Ian Soboroff, Yiqun Liu
Date: 2022-10-19 00:00:00

Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official qrels files; this paper reports results based on the corrected qrels files. The noise is due to a fatal bug in the backend of our relevance assessment interface. More specifically, at WWW-2, WWW-3, and WWW-4, two versions of pool files were created for each English topic: a PRI ("prioritised") file, which uses the NTCIRPOOL script to prioritise likely relevant documents, and a RND ("randomised") file, which randomises the pooled documents. This was done for the purpose of studying the effect of document ordering for relevance assessors. However, the programmer who wrote the interface backend assumed that a combination of a topic ID and a document rank in the pool file uniquely determines a document ID; this is obviously incorrect as we have two versions of pool files. The outcome is that all the PRI-based relevance labels for the WWW-2 test collection are incorrect (while all the RND-based relevance labels are correct), and all the RND-based relevance labels for the WWW-3 and WWW-4 test collections are incorrect (while all the PRI-based relevance labels are correct). This bug was finally discovered at the NTCIR-16 WWW-4 task when the first seven authors of this paper served as Gold assessors (i.e., topic creators who define what is relevant) and closely examined the disagreements with Bronze assessors (i.e., non-topic-creators; non-experts). We would like to apologise to the WWW participants and the NTCIR chairs for the inconvenience and confusion caused due to this bug.
Guiding Data-Driven Design Ideation by Knowledge Distance
Authors: Jianxi Luo, Serhad Sarica, Kristin Wood
Date: 2022-10-18 00:00:00
Tasks: Patent classification, Retrieval

Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge based expert system that provides design stimuli across the semantic, document and field levels simultaneously from all fields of engineering and technology and that follows creativity theories to guide the retrieval and use of stimuli according to the knowledge distance. The system is centered on the use of a network of all technology fields in the patent classification system, to store and organize the world's cumulative data on the technological knowledge, concepts, and solutions in the total patent database according to statistically estimated knowledge distance between technology fields. In turn, knowledge distance guides the network-based exploration and retrieval of inspirational stimuli for inferences across near and far fields to generate new design ideas by analogy and combination. With two case studies, we showcase the effectiveness of using the system to explore and retrieve multilevel inspirational stimuli and generate new design ideas for both problem solving and open ended innovation. These case studies also demonstrate the computer aided ideation process, which is data-driven, computationally augmented, theoretically grounded, visually inspiring, and rapid.
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Authors: Yishi Xu, Dongsheng Wang, Bo Chen, Ruiying Lu, Zhibin Duan, Mingyuan Zhou
Date: 2022-10-16 00:00:00
Tasks: Graph structure learning, Topic Models

Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embeddings to represent words and topics. With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics. Furthermore, due to the superiority of hyperbolic geometry in representing hierarchical data, tree-structure knowledge can also be naturally injected to guide the learning of a topic hierarchy. Therefore, we further develop a regularization term based on the idea of contrastive learning to inject prior structural knowledge efficiently. Experiments on both topic taxonomy discovery and document representation demonstrate that the proposed framework achieves improved performance against existing embedded topic models.
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
Authors: Mogan Gim, Donghee Choi, Kana Maruyama, Jihun Choi, Hajung Kim, Donghyeon Park, Jaewoo Kang
Date: 2022-10-14 00:00:00
Tasks: Prediction

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food affinity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fulfilling its assistive role in cuisine domain.
Digital Literacy and Reading Habits of the Central University of Tamil Nadu Students: A Survey Study
Authors: Subaveerapandiyan A, Priyanka Sinha
Date: 2022-10-14 00:00:00

The study attempted to understand the University students' digital reading habits and their related skills. It also has a view of students' preferred sources of reading, whether physical or digital resources. For this study, we conducted a survey study with students and research scholars of the Central University of Tamil Nadu, India. The instrument was a structured questionnaire distributed with various modes. The result found that the majority of the students are well known about digital tools and usage, most of the students are excellent in digital literacy skills and other findings is however they are good in digital literacy even though they like to read print books is their most favorable preference. The results conclude that whatever technological devices are developed and students have also grown their technical knowledge. The result finds out, in education especially reading-wise, students or readers' first wish is printed resources; digital books are secondary to them.
Multi-Modal Recommendation System with Auxiliary Information
Authors: Mufhumudzi Muthivhi, Terence L. Van Zyl, Hairong Wang
Date: 2022-10-13 00:00:00
Tasks: Multi-modal Recommendation, Recommendation Systems

Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available in auxiliary knowledge related to items. This study extends the existing research by evaluating a multi-modal recommendation system that exploits the inclusion of comprehensive auxiliary knowledge related to an item. The empirical results explore extracting vector representations (embeddings) from unstructured and structured data using data2vec. The fused embeddings are then used to train several state-of-the-art transformer architectures for sequential user-item representations. The analysis of the experimental results shows a statistically significant improvement in prediction accuracy, which confirms the effectiveness of including auxiliary information in a context-aware recommendation system. We report a 4% and 11% increase in the NDCG score for long and short user sequence datasets, respectively.