pwc-archive/papers-with-abstracts huggingface dataset:
Conversational Agents and Children: Let Children Learn
Authors: Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, Maria Soledad Pera
Date: 2023-02-23 00:00:00
Tasks: Position

Using online information discovery as a case study, in this position paper we discuss the need to design, develop, and deploy (conversational) agents that can -- non-intrusively -- guide children in their quest for online resources rather than simply finding resources for them. We argue that agents should "let children learn" and should be built to take on a teacher-facilitator function, allowing children to develop their technical and critical thinking abilities as they interact with varied technology in a broad range of use cases.
Metric-oriented Speech Enhancement using Diffusion Probabilistic Model
Authors: Chen Chen, Yuchen Hu, Weiwei Weng, Eng Siong Chng
Date: 2023-02-23 00:00:00
Tasks: model, Speech Enhancement

Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. However, the task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and can not be directly constructed in the training criteria. This mismatch between the training objective and evaluation metric likely results in sub-optimal performance. To alleviate it, we propose a metric-oriented speech enhancement method (MOSE), which leverages the recent advances in the diffusion probabilistic model and integrates a metric-oriented training strategy into its reverse process. Specifically, we design an actor-critic based framework that considers the evaluation metric as a posterior reward, thus guiding the reverse process to the metric-increasing direction. The experimental results demonstrate that MOSE obviously benefits from metric-oriented training and surpasses the generative baselines in terms of all evaluation metrics.
Improved Training of Mixture-of-Experts Language GANs
Authors: Yekun Chai, Qiyue Yin, Junge Zhang
Date: 2023-02-23 00:00:00
Tasks: Adversarial Text, Image Generation, Mixture-of-Experts, Text Generation

Despite the dramatic success in image generation, Generative Adversarial Networks (GANs) still face great challenges in synthesizing sequences of discrete elements, in particular human language. The difficulty in generator training arises from the limited representation capacity and uninformative learning signals obtained from the discriminator. In this work, we (1) first empirically show that the mixture-of-experts approach is able to enhance the representation capacity of the generator for language GANs and (2) harness the Feature Statistics Alignment (FSA) paradigm to render fine-grained learning signals to advance the generator training. Specifically, FSA forces the mean statistics of the distribution of fake data to approach that of real samples as close as possible in the finite-dimensional feature space. Empirical study on synthetic and real benchmarks shows the superior performance in quantitative evaluation and demonstrates the effectiveness of our approach to adversarial text generation.
Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs
Authors: Yeqin Zhang, Haomin Fu, Cheng Fu, Haiyang Yu, Yongbin Li, Cam-Tu Nguyen
Date: 2023-02-23 00:00:00
Tasks: Answer Generation, Reranking, Retrieval

Multi-document grounded dialogue systems (DGDS) belong to a class of conversational agents that answer users' requests by finding supporting knowledge from a collection of documents. Most previous studies aim to improve the knowledge retrieval model or propose more effective ways to incorporate external knowledge into a parametric generation model. These methods, however, focus on retrieving knowledge from mono-granularity language units (e.g. passages, sentences, or spans in documents), which is not enough to effectively and efficiently capture precise knowledge in long documents. This paper proposes Re3G, which aims to optimize both coarse-grained knowledge retrieval and fine-grained knowledge extraction in a unified framework. Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5). Experiments on DialDoc Shared Task demonstrate the effectiveness of our method.
Empathetic Response Generation via Emotion Cause Transition Graph
Authors: Yushan Qian, Bo wang, Ting-En Lin, Yinhe Zheng, Ying Zhu, Dongming Zhao, Yuexian Hou, Yuchuan Wu, Yongbin Li
Date: 2023-02-23 00:00:00
Tasks: Decoder, Empathetic Response Generation, Response Generation

Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.
EVJVQA Challenge: Multilingual Visual Question Answering
Authors: Ngan Luu-Thuy Nguyen, Nghia Hieu Nguyen, Duong T. D Vo, Khanh Quoc Tran, Kiet Van Nguyen
Date: 2023-02-23 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, Vietnamese Multimodal Learning, Vietnamese Visual Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers. English is a resource-rich language that has witnessed various developments in datasets and models for visual question answering. Visual question answering in other languages also would be developed for resources and models. In addition, there is no multilingual dataset targeting the visual content of a particular country with its own objects and cultural characteristics. To address the weakness, we provide the research community with a benchmark dataset named EVJVQA, including 33,000+ pairs of question-answer over three languages: Vietnamese, English, and Japanese, on approximately 5,000 images taken from Vietnam for evaluating multilingual VQA systems or models. EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022). This task attracted 62 participant teams from various universities and organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained vision model and mT5 for the pre-trained language model, a powerful pre-trained language model based on the transformer architecture. EVJVQA is a challenging dataset that motivates NLP and CV researchers to further explore the multilingual models or systems for visual question answering systems. We released the challenge on the Codalab evaluation system for further research.
Language Model Crossover: Variation through Few-Shot Prompting
Authors: Elliot Meyerson, Mark J. Nelson, Herbie Bradley, Adam Gaier, Arash Moradi, Amy K. Hoover, Joel Lehman
Date: 2023-02-23 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, model

This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e. they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e. to prompt a language model with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes' offspring. The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models. Experiments in this paper highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that language model crossover is a promising method for evolving genomes representable as text.
Particle Swarm Optimization in 3D Medical Image Registration: A Systematic Review
Authors: Lucia Ballerini
Date: 2023-02-22 00:00:00
Tasks: Image Registration, Medical Image Registration

Medical image registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. These problems usually require the optimization of a similarity metric. Swarm Intelligence techniques are very effective and efficient optimization methods. This systematic review focuses on 3D medical image registration using Particle Swarm Optimization
Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods
Authors: Tatyana Alexeeva, Quoc Bao Diep, Nikolay Kuznetsov, Ivan Zelinka
Date: 2023-02-20 00:00:00
Tasks: Decision Making, Evolutionary Algorithms, Q-Learning

One of the key tasks in the economy is forecasting the economic agents' expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model's dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency.
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow
Authors: Carles Domingo-Enrich, Aram-Alexandre Pooladian
Date: 2023-02-23 00:00:00

Let $V_* : \mathbb{R}^d \to \mathbb{R}$ be some (possibly non-convex) potential function, and consider the probability measure $\pi \propto e^{-V_*}$. When $\pi$ exhibits multiple modes, it is known that sampling techniques based on Wasserstein gradient flows of the Kullback-Leibler (KL) divergence (e.g. Langevin Monte Carlo) suffer poorly in the rate of convergence, where the dynamics are unable to easily traverse between modes. In stark contrast, the work of Lu et al. (2019; 2022) has shown that the gradient flow of the KL with respect to the Fisher-Rao (FR) geometry exhibits a convergence rate to $\pi$ is that \textit{independent} of the potential function. In this short note, we complement these existing results in the literature by providing an explicit expansion of $\text{KL}(\rho_t^{\text{FR}}\|\pi)$ in terms of $e^{-t}$, where $(\rho_t^{\text{FR}})_{t\geq 0}$ is the FR gradient flow of the KL divergence. In turn, we are able to provide a clean asymptotic convergence rate, where the burn-in time is guaranteed to be finite. Our proof is based on observing a similarity between FR gradient flows and simulated annealing with linear scaling, and facts about cumulant generating functions. We conclude with simple synthetic experiments that demonstrate our theoretical findings are indeed tight. Based on our numerics, we conjecture that the asymptotic rates of convergence for Wasserstein-Fisher-Rao gradient flows are possibly related to this expansion in some cases.
Communication-Efficient Distributed Estimation and Inference for Cox's Model
Authors: Pierre Bayle, Jianqing Fan, Zhipeng Lou
Date: 2023-02-23 00:00:00
Tasks: valid

Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals for linear combinations of high-dimensional hazard regression coefficients, we introduce a novel debiased method, establish central limit theorems, and provide consistent variance estimators that yield asymptotically valid distributed confidence intervals. In addition, we provide valid and powerful distributed hypothesis tests for any coordinate element based on a decorrelated score test. We allow time-dependent covariates as well as censored survival times. Extensive numerical experiments on both simulated and real data lend further support to our theory and demonstrate that our communication-efficient distributed estimators, confidence intervals, and hypothesis tests improve upon alternative methods.
A Comparison of Modeling Preprocessing Techniques
Authors: Tosan Johnson, Alice J. Liu, Syed Raza, Aaron McGuire
Date: 2023-02-23 00:00:00
Tasks: Binary Classification, Feature Importance, feature selection, Imputation

This paper compares the performance of various data processing methods in terms of predictive performance for structured data. This paper also seeks to identify and recommend preprocessing methodologies for tree-based binary classification models, with a focus on eXtreme Gradient Boosting (XGBoost) models. Three data sets of various structures, interactions, and complexity were constructed, which were supplemented by a real-world data set from the Lending Club. We compare several methods for feature selection, categorical handling, and null imputation. Performance is assessed using relative comparisons among the chosen methodologies, including model prediction variability. This paper is presented by the three groups of preprocessing methodologies, with each section consisting of generalized observations. Each observation is accompanied by a recommendation of one or more preferred methodologies. Among feature selection methods, permutation-based feature importance, regularization, and XGBoost's feature importance by weight are not recommended. The correlation coefficient reduction also shows inferior performance. Instead, XGBoost importance by gain shows the most consistency and highest caliber of performance. Categorical featuring encoding methods show greater discrimination in performance among data set structures. While there was no universal "best" method, frequency encoding showed the greatest performance for the most complex data sets (Lending Club), but had the poorest performance for all synthetic (i.e., simpler) data sets. Finally, missing indicator imputation dominated in terms of performance among imputation methods, whereas tree imputation showed extremely poor and highly variable model performance.
Solving Recurrent MIPs with Semi-supervised Graph Neural Networks
Authors: Konstantinos Benidis, Ugo Rosolia, Syama Rangapuram, George Iosifidis, Georgios Paschos
Date: 2023-02-20 00:00:00

We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables. Our approach is motivated by the observation that many problem instances share salient features and solution structures since they differ only in few (time-varying) parameters. Examples include transportation and routing problems where decisions need to be re-optimized whenever commodity volumes or link costs change. Our method is the first to exploit the sequential nature of the instances being solved periodically, and can be trained with ``unlabeled'' instances, when exact solutions are unavailable, in a semi-supervised setting. Also, we provide a principled way of transforming the probabilistic predictions into integral solutions. Using a battery of experiments with representative binary MIPs, we show the gains of our model over other ML-based optimization approaches.
Automated Statement Extraction from Press Briefings
Authors: Jüri Keller, Meik Bittkowski, Philipp Schaer
Date: 2023-02-23 00:00:00

Scientific press briefings are a valuable information source. They consist of alternating expert speeches, questions from the audience and their answers. Therefore, they can contribute to scientific and fact-based media coverage. Even though press briefings are highly informative, extracting statements relevant to individual journalistic tasks is challenging and time-consuming. To support this task, an automated statement extraction system is proposed. Claims are used as the main feature to identify statements in press briefing transcripts. The statement extraction task is formulated as a four-step procedure. First, the press briefings are split into sentences and passages, then claim sentences are identified through sequence classification. Subsequently, topics are detected, and the sentences are filtered to improve the coherence and assess the length of the statements. The results indicate that claim detection can be used to identify statements in press briefings. While many statements can be extracted automatically with this system, they are not always as coherent as needed to be understood without context and may need further review by knowledgeable persons.
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit
Authors: Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, Xiaoyan Li, Yuxiao Dong, Jie Tang
Date: 2023-02-23 00:00:00
Tasks: Data Integration

Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 authors. Such real-world challenging cases have not been effectively addressed by existing researches due to the small-scale or low-quality datasets that they have used. The development of effective algorithms is further hampered by a variety of tasks and evaluation protocols designed on top of diverse datasets. To this end, we present WhoIsWho owning, a large-scale benchmark with over 1,000,000 papers built using an interactive annotation process, a regular leaderboard with comprehensive tasks, and an easy-to-use toolkit encapsulating the entire pipeline as well as the most powerful features and baseline models for tackling the tasks. Our developed strong baseline has already been deployed online in the AMiner system to enable daily arXiv paper assignments. The public leaderboard is available at http://whoiswho.biendata.xyz/. The toolkit is at https://github.com/THUDM/WhoIsWho. The online demo of daily arXiv paper assignments is at https://na-demo.aminer.cn/arxivpaper.
Cost-Sensitive Active Learning for Incomplete Data
Authors: Min Wang, Chunyu Yang, Fei Zhao, Fan Min, XiZhao Wang
Date: 2022-06-23 00:00:00
Proceeding: IEEE Transactions on Systems, Man, and Cybernetics: Systems 2022 6
Tasks: Active Learning, Attribute, Imputation

Practical data often suffer from missing attribute values and lack of class labels. A reasonable machine learning scenario involves obtaining certain values and labels at cost on request. In this article, we propose the cost-sensitive active learning through unified evaluation and dynamic selection (CALS) algorithm to handle the learning task in this new scenario. For data representation, we consider misclassification cost, label query cost, and attribute query cost. For the cost/benefit estimation, we design a unified assessment of attribute values and labels with softmax regression. For the selection of attribute value and label, we propose an optimal acquisition scheme with permutation and greedy strategies. We perform experiments with synthetic, benchmark, and domain datasets. The results of the significance test verify the effectiveness of CALS and its superiority over cost-sensitive active learning and missing data imputation algorithms.
Depthwise Separable Temporal Convolutional Network for Action Segmentation
Authors: Basavaraj Hampiholi, Christian Jarvers, Wolfgang Mader, Heiko Neumann
Date: 2021-01-19 00:00:00
Proceeding: 2020 International Conference on 3D Vision (3DV) 2021 1
Tasks: Action Segmentation, Decoder, Temporal Action Segmentation

Fine-grained temporal action segmentation in long,
untrimmed RGB videos is a key topic in visual human-
machine interaction. Recent temporal convolution based
approaches either use encoder-decoder(ED) architecture or
dilations with doubling factor in consecutive convolution
layers to segment actions in videos. However ED networks
operate on low temporal resolution and the dilations in suc-
cessive layers cause gridding artifacts problem. We propose
depthwise separable temporal convolution network (DS-
TCN) that operates on full temporal resolution and with re-
duced gridding effects. The basic component of DS-TCN
is residual depthwise dilated block (RDDB). We explore the
trade-off between large kernels and small dilation rates us-
ing RDDB. We show that our DS-TCN is capable of captur-
ing long-term dependencies as well as local temporal cues
efficiently. Our evaluation on three benchmark datasets,
GTEA, 50Salads, and Breakfast demonstrates that DS-TCN
outperforms the existing ED-TCN and dilation based TCN
baselines even with comparatively fewer parameters.
Optimal Controller and Security Parameter for Encrypted Control Systems Under Least Squares Identification
Authors: Kaoru Teranishi, Kiminao Kogiso
Date: 2023-02-23 00:00:00

Encrypted control is a framework for the secure outsourcing of controller computation using homomorphic encryption that allows to perform arithmetic operations on encrypted data without decryption. In a previous study, the security level of encrypted control systems was quantified based on the difficulty and computation time of system identification. This study investigates an optimal design of encrypted control systems when facing an attack attempting to estimate a system parameter by the least squares method from the perspective of the security level. This study proposes an optimal $H_2$ controller that maximizes the difficulty of estimation and an equation to determine the minimum security parameter that guarantee the security of an encrypted control system as a solution to the design problem. The proposed controller and security parameter are beneficial for reducing the computation costs of an encrypted control system, while achieving the desired security level. Furthermore, the proposed design method enables the systematic design of encrypted control systems.