pwc-archive/papers-with-abstracts huggingface dataset:
Trimming Phonetic Alignments Improves the Inference of Sound Correspondence Patterns from Multilingual Wordlists
Authors: Frederic Blum, Johann-Mattis List
Date: 2023-03-31 00:00:00

Sound correspondence patterns form the basis of cognate detection and phonological reconstruction in historical language comparison. Methods for the automatic inference of correspondence patterns from phonetically aligned cognate sets have been proposed, but their application to multilingual wordlists requires extremely well annotated datasets. Since annotation is tedious and time consuming, it would be desirable to find ways to improve aligned cognate data automatically. Taking inspiration from trimming techniques in evolutionary biology, which improve alignments by excluding problematic sites, we propose a workflow that trims phonetic alignments in comparative linguistics prior to the inference of correspondence patterns. Testing these techniques on a large standardized collection of ten datasets with expert annotations from different language families, we find that the best trimming technique substantially improves the overall consistency of the alignments. The results show a clear increase in the proportion of frequent correspondence patterns and words exhibiting regular cognate relations.
JobHam-place with smart recommend job options and candidate filtering options
Authors: Shiyao Wu
Date: 2023-03-31 00:00:00
Tasks: Keyword Extraction

Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, which are not integrated into the system. Thus, a smart job hunter combined with the above functionality will be conducted in this project, which contains job recommendations, CV ranking and even a job dashboard for skills and job applicant functionality. Job recommendation and CV ranking starts from the automatic keyword extraction and end with the Job/CV ranking algorithm. Automatic keyword extraction is implemented by Job2Skill and the CV2Skill model based on Bert. Job2Skill consists of two components, text encoder and Gru-based layers, while CV2Skill is mainly based on Bert and fine-tunes the pre-trained model by the Resume- Entity dataset. Besides, to match skills from CV and job description and rank lists of jobs and candidates, job/CV ranking algorithms have been provided to compute the occurrence ratio of skill words based on TFIDF score and match ratio of the total skill numbers. Besides, some advanced features have been integrated into the website to improve user experiences, such as the calendar and sweetalert2 plugin. And some basic features to go through job application processes, such as job application tracking and interview arrangement.
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Authors: Li Zhou, Laura Cabello, Yong Cao, Daniel Hershcovich
Date: 2023-03-31 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Few-Shot Learning, Transfer Learning

Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
Authors: Min Liu, Yu Bao, Chengqi Zhao, ShuJian Huang
Date: 2023-03-31 00:00:00
Tasks: Knowledge Distillation, Machine Translation, Translation

Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. However, existing knowledge distillation has side effects, such as propagating errors from the teacher to NAT students, which may limit further improvements of NAT models and are rarely discussed in existing research. In this paper, we introduce selective knowledge distillation by introducing an NAT evaluator to select NAT-friendly targets that are of high quality and easy to learn. In addition, we introduce a simple yet effective progressive distillation method to boost NAT performance. Experiment results on multiple WMT language directions and several representative NAT models show that our approach can realize a flexible trade-off between the quality and complexity of training data for NAT models, achieving strong performances. Further analysis shows that distilling only 5% of the raw translations can help an NAT outperform its counterpart trained on raw data by about 2.4 BLEU.
WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset
Authors: Tiago Ribeiro, Stephanie Brandl, Anders Søgaard, Nora Hollenstein
Date: 2023-03-31 00:00:00
Tasks: Question Answering

We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models. WebQAmGaze includes webcam eye-tracking data from 600 participants of a wide age range naturally reading English, German, Spanish, and Turkish texts. Each participant performs two reading tasks composed of five texts each, a normal reading and an information-seeking task, followed by a comprehension question. We compare the collected webcam data to high-quality eye-tracking recordings. The results show a moderate to strong correlation between the eye movement measures obtained with the webcam compared to those obtained with a commercial eye-tracking device. When validating the data, we find that higher fixation duration on relevant text spans accurately indicates correctness when answering the corresponding questions. This dataset advances webcam-based reading studies and opens avenues to low-cost and diverse data collection. WebQAmGaze is beneficial to learn about the cognitive processes behind question-answering and to apply these insights to computational models of language understanding.
Can AI Put Gamma-Ray Astrophysicists Out of a Job?
Authors: Samuel T. Spencer, Vikas Joshi, Alison M. W. Mitchell
Date: 2023-03-31 00:00:00

In what will likely be a litany of generative-model-themed arXiv submissions celebrating April the 1st, we evaluate the capacity of state-of-the-art transformer models to create a paper detailing the detection of a Pulsar Wind Nebula with a non-existent Imaging Atmospheric Cherenkov Telescope (IACT) Array. We do this to evaluate the ability of such models to interpret astronomical observations and sources based on language information alone, and to assess potential means by which fraudulently generated scientific papers could be identified during peer review (given that reliable generative model watermarking has yet to be deployed for these tools). We conclude that our jobs as astronomers are safe for the time being. From this point on, prompts given to ChatGPT and Stable Diffusion are shown in orange, text generated by ChatGPT is shown in black, whereas analysis by the (human) authors is in blue.
Dialog act guided contextual adapter for personalized speech recognition
Authors: Feng-Ju Chang, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra, Kai Wei, Grant P. Strimel, Ross McGowan
Date: 2023-03-31 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network. Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing. On industrial voice assistant datasets, our model outperforms both the baselines - dialog act encoder-only model, and the contextual adaptation, leading to the most improvement over the no-context model: 58% average relative word error rate reduction (WERR) in the multi-turn dialog scenario, in comparison to the prior-art contextual adapter, which has achieved 39% WERR over the no-context model.
Design by Contract Framework for Quantum Software
Authors: Masaomi Yamaguchi, Nobukazu Yoshioka
Date: 2023-03-31 00:00:00

To realize reliable quantum software, techniques to automatically ensure the quantum software's correctness have recently been investigated. However, they primarily focus on fixed quantum circuits rather than the procedure of building quantum circuits. Despite being a common approach, the correctness of building circuits using different parameters following the same procedure is not guaranteed. To this end, we propose a design-by-contract framework for quantum software. Our framework provides a python-embedded language to write assertions on the input and output states of all quantum circuits built by certain procedures. Additionally, it provides a method to write assertions about the statistical processing of measurement results to ensure the procedure's correctness for obtaining the final result. These assertions are automatically checked using a quantum computer simulator. For evaluation, we implemented our framework and wrote assertions for some widely used quantum algorithms. Consequently, we found that our framework has sufficient expressive power to verify the whole procedure of quantum software.
What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions
Authors: Shih-Hong Huang, Chieh-Yang Huang, Ya-Fang Lin, Ting-Hao 'Kenneth' Huang
Date: 2023-03-30 00:00:00
Tasks: Spoken Dialogue Systems

The proliferation of automated conversational systems such as chatbots, spoken-dialogue systems, and smart speakers, has significantly impacted modern digital life. However, these systems are primarily designed to provide answers to well-defined questions rather than to support users in exploring complex, ill-defined questions. In this paper, we aim to push the boundaries of conversational systems by examining the types of nebulous, open-ended questions that can best be answered through conversation. We first sampled 500 questions from one million open-ended requests posted on AskReddit, and then recruited online crowd workers to answer eight inquiries about these questions. We also performed open coding to categorize the questions into 27 different domains. We found that the issues people believe require conversation to resolve satisfactorily are highly social and personal. Our work provides insights into how future research could be geared to align with users' needs.
Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms
Authors: Mayank Soni, Vincent Wade
Date: 2023-03-30 00:00:00
Tasks: Abstractive Text Summarization, text-classification, Text Classification, Text Generation

Large Language Models (LLMs) have gathered significant attention due to their impressive performance on a variety of tasks. ChatGPT, developed by OpenAI, is a recent addition to the family of language models and is being called a disruptive technology by a few, owing to its human-like text-generation capabilities. Although, many anecdotal examples across the internet have evaluated ChatGPT's strength and weakness, only a few systematic research studies exist. To contribute to the body of literature of systematic research on ChatGPT, we evaluate the performance of ChatGPT on Abstractive Summarization by the means of automated metrics and blinded human reviewers. We also build automatic text classifiers to detect ChatGPT generated summaries. We found that while text classification algorithms can distinguish between real and generated summaries, humans are unable to distinguish between real summaries and those produced by ChatGPT.
Hypothesis testing on invariant subspaces of non-symmetric matrices with applications to network statistics
Authors: Jérôme R. Simons
Date: 2023-03-31 00:00:00

We extend the inference procedure for eigenvectors of Tyler (1981), which assumes symmetrizable matrices to generic invariant and singular subspaces of non-diagonalisable matrices to test whether $\nu \in \mathbb{R}^{p \times r}$ is an element of an invariant subspace of $M \in \mathbb{R}^{p \times p}$. Our results include a Wald test for full-vector hypotheses and a $t$-test for coefficient-wise hypotheses. We employ perturbation expansions of invariant subspaces from Sun (1991) and singular subspaces from Liu et al. (2007). Based on the former, we extend the popular Davis-Kahan bound to estimations of its higher-order polynomials and study how the bound simplifies for eigenspaces but attains complexity for generic invariant subspaces.
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo
Authors: Jakob Robnik, Uroš Seljak
Date: 2023-03-31 00:00:00

Stochastic sampling algorithms such as Langevin Monte Carlo are inspired by physical systems in a heat bath. Their equilibrium distribution is the canonical ensemble given by a prescribed target distribution, so they must balance fluctuation and dissipation as dictated by the fluctuation-dissipation theorem. In contrast to the common belief, we show that the fluctuation-dissipation theorem is not required because only the configuration space distribution, and not the full phase space distribution, needs to be canonical. We propose a continuous-time Microcanonical Langevin Monte Carlo (MCLMC) as a dissipation-free system of stochastic differential equations (SDE). We derive the corresponding Fokker-Planck equation and show that the stationary distribution is the microcanonical ensemble with the desired canonical distribution on configuration space. We prove that MCLMC is ergodic for any nonzero amount of stochasticity, and for smooth, convex potentials, the expectation values converge exponentially fast. Furthermore, the deterministic drift and the stochastic diffusion separately preserve the stationary distribution. This uncommon property is attractive for practical implementations as it implies that the drift-diffusion discretization schemes are bias-free, so the only source of bias is the discretization of the deterministic dynamics. We applied MCLMC on a lattice $\phi^4$ model, where Hamiltonian Monte Carlo (HMC) is currently the state-of-the-art integrator. For the same accuracy, MCLMC converges 12 times faster than HMC on an $8\times8$ lattice. On a $64\times64$ lattice, it is already 32 times faster. The trend is expected to persist to larger lattices, which are of particular interest, for example, in lattice quantum chromodynamics.
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability
Authors: Arnab Auddy, Ming Yuan
Date: 2023-03-31 00:00:00

In this paper, we investigate the optimal statistical performance and the impact of computational constraints for independent component analysis (ICA). Our goal is twofold. On the one hand, we characterize the precise role of dimensionality on sample complexity and statistical accuracy, and how computational consideration may affect them. In particular, we show that the optimal sample complexity is linear in dimensionality, and interestingly, the commonly used sample kurtosis-based approaches are necessarily suboptimal. However, the optimal sample complexity becomes quadratic, up to a logarithmic factor, in the dimension if we restrict ourselves to estimates that can be computed with low-degree polynomial algorithms. On the other hand, we develop computationally tractable estimates that attain both the optimal sample complexity and minimax optimal rates of convergence. We study the asymptotic properties of the proposed estimates and establish their asymptotic normality that can be readily used for statistical inferences. Our method is fairly easy to implement and numerical experiments are presented to further demonstrate its practical merits.
Synergistic Graph Fusion via Encoder Embedding
Authors: Cencheng Shen, Carey E. Priebe, Jonathan Larson, Ha Trinh
Date: 2023-03-31 00:00:00
Tasks: Classification, Graph Embedding, Stochastic Block Model

In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect: for sufficiently large vertex size, the accuracy of vertex classification consistently benefits from the incorporation of additional graphs. We establish the mathematical foundation for the method, including the asymptotic convergence of the embedding, a sufficient condition for asymptotic optimal classification, and the proof of the synergistic effect for vertex classification. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method, showcasing the pronounced synergistic effect for multiple graphs from disparate sources.
Distributed Subgradient Algorithm for Multi-Agent Optimization With Dynamic Stepsize
Authors: Xiaoxing Ren, Dewei Li, Yugeng Xi, Haibin Shao
Date: 2021-02-19 00:00:00
Proceeding: IEEE/CAA Journal of Automatica Sinica 2021 2

In this paper, we consider distributed convex optimization problems on multi-agent networks. We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing the time-varying estimate of the local function value at the global optimal solution. Our approach can be applied to both synchronous and asynchronous communication protocols. Specifically, we propose the distributed subgradient with uncoordinated dynamic stepsizes (DS-UD) algorithm for synchronous protocol and the AsynDGD algorithm for asynchronous protocol. Theoretical analysis shows that the proposed algorithms guarantee that all agents reach a consensus on the solution to the multi-agent optimization problem. Moreover, the proposed approach with dynamic stepsizes eliminates the requirement of diminishing stepsize in existing works. Numerical examples of distributed estimation in sensor networks are provided to illustrate the effectiveness of the proposed approach.
Self-Supervised Music-Motion Synchronization Learning for Music-Driven Conducting Motion Generation
Authors: Fan Liu, Delong Chen, Ruizhi Zhou, Sai Yang, Feng Xu
Date: 2022-05-31 00:00:00
Proceeding: Journal of Computer Science and Technology 2022 5
Tasks: Generative Adversarial Network, Motion Generation

The correlation between music and human motion has attracted widespread research attention. Although recent studies have successfully generated motion for singers, dancers, and musicians, few have explored motion generation for orchestral conductors. The generation of music-driven conducting motion should consider not only the basic music beats, but also mid-level music structures, high-level music semantic expressions, and hints for different parts of orchestras (strings, woodwind, etc.). However, most existing conducting motion generation methods rely heavily on human-designed rules, which significantly limits the quality of generated motion. Therefore, we propose a novel Music Motion Synchronized Generative Adversarial Network (M2S-GAN), which generates motions according to the automatically learned music representations. More specifically, M2S-GAN is a cross-modal generative network comprising four components: 1) a music encoder that encodes the music signal; 2) a generator that generates conducting motion from the music codes; 3) a motion encoder that encodes the motion; 4) a discriminator that differentiates the real and generated motions. These four components respectively imitate four key aspects of human conductors: understanding music, interpreting music, precision and elegance. The music and motion encoders are first jointly trained by a self-supervised contrastive loss, and can thus help to facilitate the music motion synchronization during the following adversarial learning process. To verify the effectiveness of our method, we construct a large-scale dataset, named ConductorMotion100, which consists of unprecedented 100 hours of conducting motion data. Extensive experiments on ConductorMotion100 demonstrate the effectiveness of M2S-GAN. Our proposed approach outperforms various comparison methods both quantitatively and qualitatively. Through visualization, we show that our approach can generate plausible, diverse, and music-synchronized conducting motion.
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection
Authors: Miel Verkerken, Laurens D’hooge, Didik Sudyana, Ying-Dar Lin, Tim Wauters, Bruno Volckaert, Filip De Turck
Date: 2023-03-21 00:00:00
Proceeding: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023 3
Tasks: Anomaly Detection, Intrusion Detection, Multi-class Classification, Network Intrusion Detection

An intrusion detection system (IDS), traditionally an example of an effective security monitoring system, is facing significant challenges due to the ongoing digitization of our modern society. The growing number and variety of connected devices are not only causing a continuous emergence of new threats that are not recognized by existing systems, but the amount of data to be monitored is also exceeding the capabilities of a single system. This raises the need for a scalable IDS capable of detecting unknown, zero-day, attacks. In this paper, a novel multi-stage approach for hierarchical intrusion detection is proposed. The proposed approach is validated on the
public benchmark datasets, CIC-IDS-2017 and CSE-CIC-IDS-2018. Results demonstrate that our proposed approach besides effective and robust zero-day detection, outperforms both the baseline and existing approaches, achieving high classification performance, up to 96% balanced accuracy. Additionally, the proposed approach is easily adaptable without any retraining and takes advantage of n-tier deployments to reduce bandwidth and computational requirements while preserving privacy constraints. The best-performing models with a balanced set of thresholds correctly classified 87% or 41 out of 47 zero-day attacks, while reducing the bandwidth requirements up to 69%.