pwc-archive/papers-with-abstracts huggingface dataset:
Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement
Authors: Zhen Yang, Fandong Meng, Yuanmeng Yan, Jie zhou
Date: 2022-09-13 00:00:00
Tasks: Machine Translation, Sentence, TAG, Translation

Word-level Quality Estimation (QE) of Machine Translation (MT) aims to find out potential translation errors in the translated sentence without reference. Typically, conventional works on word-level QE are designed to predict the translation quality in terms of the post-editing effort, where the word labels ("OK" and "BAD") are automatically generated by comparing words between MT sentences and the post-edited sentences through a Translation Error Rate (TER) toolkit. While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with the human judgement on whether the word is well or poorly translated. To overcome the limitation, we first create a golden benchmark dataset, namely \emph{HJQE} (Human Judgement on Quality Estimation), where the expert translators directly annotate the poorly translated words on their judgements. Additionally, to further make use of the parallel corpus, we propose the self-supervised pre-training with two tag correcting strategies, namely tag refinement strategy and tree-based annotation strategy, to make the TER-based artificial QE corpus closer to \emph{HJQE}. We conduct substantial experiments based on the publicly available WMT En-De and En-Zh corpora. The results not only show our proposed dataset is more consistent with human judgment but also confirm the effectiveness of the proposed tag correcting strategies.\footnote{The data can be found at \url{https://github.com/ZhenYangIACAS/HJQE}.}
An Embedding-Based Grocery Search Model at Instacart
Authors: Yuqing Xie, Taesik Na, Xiao Xiao, Saurav Manchanda, Young Rao, Zhihong Xu, Guanghua Shu, Esther Vasiete, Tejaswi Tenneti, Haixun Wang
Date: 2022-09-12 00:00:00
Tasks: model

The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system learns query and product representations with a two-tower transformer-based encoder architecture. To tackle the cold-start problem, we focus on content-based features. To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method. AccOn an offline human evaluation dataset, we achieve 10% relative improvement in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search (CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we train and deploy the embedding based search model and give a detailed analysis of the effectiveness of our method.
Uncovering Regions of Maximum Dissimilarity on Random Process Data
Authors: Miguel de Carvalho, Gabriel Martos Venturini
Date: 2022-09-12 00:00:00
Tasks: Bayesian Inference, Point Processes, Time Series, Time Series Analysis

The comparison of local characteristics of two random processes can shed light on periods of time or space at which the processes differ the most. This paper proposes a method that learns about regions with a certain volume, where the marginal attributes of two processes are less similar. The proposed methods are devised in full generality for the setting where the data of interest are themselves stochastic processes, and thus the proposed method can be used for pointing out the regions of maximum dissimilarity with a certain volume, in the contexts of functional data, time series, and point processes. The parameter functions underlying both stochastic processes of interest are modeled via a basis representation, and Bayesian inference is conducted via an integrated nested Laplace approximation. The numerical studies validate the proposed methods, and we showcase their application with case studies on criminology, finance, and medicine.
Entity Tagging: Extracting Entities in Text Without Mention Supervision
Authors: Christina Du, Kashyap Popat, Louis Martin, Fabio Petroni
Date: 2022-09-13 00:00:00
Tasks: Entity Linking

Detection and disambiguation of all entities in text is a crucial task for a wide range of applications. The typical formulation of the problem involves two stages: detect mention boundaries and link all mentions to a knowledge base. For a long time, mention detection has been considered as a necessary step for extracting all entities in a piece of text, even if the information about mention spans is ignored by some downstream applications that merely focus on the set of extracted entities. In this paper we show that, in such cases, detection of mention boundaries does not bring any considerable performance gain in extracting entities, and therefore can be skipped. To conduct our analysis, we propose an "Entity Tagging" formulation of the problem, where models are evaluated purely on the set of extracted entities without considering mentions. We compare a state-of-the-art mention-aware entity linking solution against GET, a mention-agnostic sequence-to-sequence model that simply outputs a list of disambiguated entities given an input context. We find that these models achieve comparable performance when trained both on a fully and partially annotated dataset across multiple benchmarks, demonstrating that GET can extract disambiguated entities with strong performance without explicit mention boundaries supervision.
SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
Authors: Eunseong Choi, Sunkyung Lee, Minjin Choi, Hyeseon Ko, Young-In Song, Jongwuk Lee
Date: 2022-09-13 00:00:00
Tasks: Retrieval

Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc search but incurs the vocabulary mismatch problem. Although recent neural ranking models using pre-trained language models can address this problem, they usually require expensive query inference costs, implying the trade-off between effectiveness and efficiency. Tackling the trade-off, we propose a novel uni-encoder ranking model, Sparse retriever using a Dual document Encoder (SpaDE), learning document representation via the dual encoder. Each encoder plays a central role in (i) adjusting the importance of terms to improve lexical matching and (ii) expanding additional terms to support semantic matching. Furthermore, our co-training strategy trains the dual encoder effectively and avoids unnecessary intervention in training each other. Experimental results on several benchmarks show that SpaDE outperforms existing uni-encoder ranking models.
No existence of linear algorithm for Fourier phase retrieval
Authors: Meng Huang, Zhiqiang Xu
Date: 2022-09-13 00:00:00
Tasks: Retrieval

Fourier phase retrieval, which seeks to reconstruct a signal from its Fourier magnitude, is of fundamental importance in fields of engineering and science. In this paper, we give a theoretical understanding of algorithms for Fourier phase retrieval. Particularly, we show if there exists an algorithm which could reconstruct an arbitrary signal ${\mathbf x}\in {\mathbb C}^N$ in $ \mbox{Poly}(N) \log(1/\epsilon)$ time to reach $\epsilon$-precision from its magnitude of discrete Fourier transform and its initial value $x(0)$, then $\mathcal{ P}=\mathcal{NP}$. This demystifies the phenomenon that, although almost all signals are determined uniquely by their Fourier magnitude with a prior conditions, there is no algorithm with theoretical guarantees being proposed over the past few decades. Our proofs employ the result in computational complexity theory that Product Partition problem is NP-complete in the strong sense.
A Stakeholder-Centered View on Fairness in Music Recommender Systems
Authors: Karlijn Dinnissen, Christine Bauer
Date: 2022-09-08 00:00:00
Tasks: Fairness, Recommendation Systems

Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain.
User recommendation system based on MIND dataset
Authors: Niran A. Abdulhussein, Ahmed J Obaid
Date: 2022-09-06 00:00:00
Proceeding: International Journal of Nonlinear Analysis and Applications 2022 9
Tasks: Collaborative Filtering, News Recommendation, Recommendation Systems, Word Embeddings

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.
A Scalable Recommendation Engine for New Users and Items
Authors: Boya Xu, Yiting Deng, Carl Mela
Date: 2022-09-06 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face several challenges: i) how to make initial recommendations to users with little or no response history (i.e., cold-start problem), ii) how to learn user preferences on items (test and learn), and iii) how to scale across many users and items with myriad demographics and attributes. While many recommendation systems accommodate aspects of these challenges, few if any address all. This paper introduces a Collaborative Filtering (CF) Multi-armed Bandit (B) with Attributes (A) recommendation system (CFB-A) to jointly accommodate all of these considerations. Empirical applications including an offline test on MovieLens data, synthetic data simulations, and an online grocery experiment indicate the CFB-A leads to substantial improvement on cumulative average rewards (e.g., total money or time spent, clicks, purchased quantities, average ratings, etc.) relative to the most powerful extant baseline methods.
Hierarchical Conversational Preference Elicitation with Bandit Feedback
Authors: Jinhang Zuo, Songwen Hu, Tong Yu, Shuai Li, Handong Zhao, Carlee Joe-Wong
Date: 2022-09-06 00:00:00
Tasks: Recommendation Systems

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories. Most existing conversational recommender systems for cold-start users utilize a multi-armed bandit framework to learn users' preference in an online manner. However, they rely on a pre-defined conversation frequency for asking about item categories instead of individual items, which may incur excessive conversational interactions that hurt user experience. To enable more flexible questioning about key-terms, we formulate a new conversational bandit problem that allows the recommender system to choose either a key-term or an item to recommend at each round and explicitly models the rewards of these actions. This motivates us to handle a new exploration-exploitation (EE) trade-off between key-term asking and item recommendation, which requires us to accurately model the relationship between key-term and item rewards. We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. We theoretically prove that our algorithm can reduce the regret bound's dependency on the total number of items from previous work. We validate our proposed algorithms and regret bound on both synthetic and real-world data.
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Authors: Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang, Biye Jiang, Shuguang Han, Hongbo Deng, Bo Zheng
Date: 2022-09-04 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks. In the context of Click-Through Rate (CTR) prediction, we observe an interesting one-epoch overfitting problem: the model performance exhibits a dramatic degradation at the beginning of the second epoch. Such a phenomenon has been witnessed widely in real-world applications of CTR models. Thereby, the best performance is usually achieved by training with only one epoch. To understand the underlying factors behind the one-epoch phenomenon, we conduct extensive experiments on the production data set collected from the display advertising system of Alibaba. The results show that the model structure, the optimization algorithm with a fast convergence rate, and the feature sparsity are closely related to the one-epoch phenomenon. We also provide a likely hypothesis for explaining such a phenomenon and conduct a set of proof-of-concept experiments. We hope this work can shed light on future research on training more epochs for better performance.
Integrated Multiport Bidirectional DC-DC Converter for HEV/FCV Applications
Authors: Bang Le-Huy Nguyen, Honnyong Cha, Tuyen Vu, Thai-Thanh Nguyen
Date: 2022-09-13 00:00:00

This paper proposes a novel integrated multiport bidirectional dc-dc converter to interface the battery, the ultra-capacitor, the fuel cell, or other energy sources with the dc-link capacitor of the hybrid energy systems such as the hybrid electric vehicle (HEV) and fuel cell vehicle (FCV) applications. The proposed converter can be applied to the distributed generation systems which include local energy sources, storage, and loads. It can perform both buck and boost functions with fewer switches. In addition, it is extendable when more inputs and/or outputs are required. The operating principle and control strategy of the proposed converter will be analyzed in detail. For verification, simulation, and experimental results of the four utilized operating modes of an HEV/FCV are provided.
Power Converter Topologies for Electrolyzer Applications to Enable Electric Grid Services
Authors: Bang L. H. Nguyen, Mayank Panwar, Rob Hovsapian, Kazunori Nagasawa, Tuyen V. Vu
Date: 2022-09-13 00:00:00
Tasks: Unity

Hydrogen electrolyzers, with their operational flexibility, can be configured as smart dynamic loads which can provide grid services and facilitate the integration of more renewable energy sources into the electrical grid. However, to enable this ability, the electrolyzer system should be able to control both active and reactive power in coordination with the low-level controller of the electrolyzer via the power electronics system interface between the utility grid and electrolyzer. This paper discusses power converter topologies and the control scheme of this power electronics interface for electrolyzer applications to enable electricity grid services. For the sake of unity, in this paper, we consider the power converter system interfacing the utility grid at the line-to-line root mean square RMS value of 480 VAC 60 Hz and supplying to the 3500 A 750 kW PEM electrolyzer stack.
Integrated Multiport Back-to-Back Power Converter for Type-4 Wind Turbine Generator with Hybrid Energy Storage System
Authors: Bang Le-Huy Nguyen, Thai-Thanh Nguyen, Van-Long Pham, Tuyen Vu, Mayank Panwar, Rob Hovsapian
Date: 2022-09-13 00:00:00

This paper proposes a novel integrated multiport bidirectional back-to-back power converter for a type-4 wind turbine that accommodates a battery and supercapacitor for energy storage. The circuit topology reduces 4 switches compared to the traditional configuration. Moreover, owing to the dual-buck structure embedded in the phase leg, the circuitry has no short-circuit path, therefore it withstands short-circuited events for a much longer time than the normal phase-leg and prevents the reverse current in turn-off recovery. The use of a hybrid energy storage system with battery and supercapacitor helps smooth out the power output under wind gusts and stabilizes the DC-link voltage under grid fault conditions. The case studies are carried out with a 1.5 MW wind turbine system. Simulation results are provided for the theoretical validation.
Spacecraft Attitude Pointing Control under Pointing Forbidden Constraints with Guaranteed Accuracy
Authors: Jiakun Lei, Tao Meng, Weijia Wang, Shujian Sun, Heng Li, Zhonghe Jin
Date: 2022-09-13 00:00:00

This paper focuses on the attitude pointing control problem under pointing-forbidden constraints and performance constraints. The spacecraft is expected to align its sensor's boresight to a desired direction, while the terminal control accuracy and the attitude adjustment rapidity should also be guaranteed simultaneously. To resolve this problem, a switching controller structure is proposed in this paper based on the reduced-attitude representation, fusing the artificial potential field (APF) methodology and the Prescribed Performance Control (PPC) scheme together. Firstly, a novel artificial potential field is presented, and a particular function is designed for the mollification of the switching process, aiming at providing a smooth transition for the system status. Subsequently, we propose a special performance function, which can freeze the PPC part when necessary. In this way, the intrinsic contradictory between the fast attitude maneuver and forbidden direction avoidance is tackled Further, an asynchronous switching strategy is designed, guarantees the system's stability. Based on these proposed issues, a switching backstepping controller is developed, and a tracking differentiator(TD) is employed to generate a smooth approximation of differential signals. Numerical simulation results are illustrated to show the effectiveness of the proposed scheme.
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Authors: Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos
Date: 2022-09-13 00:00:00
Tasks: Interpretable Machine Learning

Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used. Second, we seek to demonstrate that SHAP explanations are able to capture the underlying physics of the power system. To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values. To do so, we take the derivatives of SHAP values from a ML model trained to learn line flows from generator power injections, using a simple DC power flow case in the 9-bus 3-generator test network. In demonstrating that SHAP values can be related back to the physics that underpin the power system, we build confidence in the explanations SHAP can offer.
Design and Simulation-Based Testing of Connected Traffic Light Guidance Systems
Authors: Michael Khayyat, Alberto Gabriele, Francesca Mancini, Stefano Arrigoni, Francesco Braghin
Date: 2022-09-13 00:00:00

The establishment of fast and reliable communication technologies, such as 5G, is enabling the evolution of a new generation of connected ADAS. This work aims to develop a traffic light advisory system, Multiple Traffic Light Advisor (MTLA), to improve driving efficiency and intersection viability, and reduce urban pollution. The developed system guides the driver on how to modify the vehicle speed to efficiently utilize the current and future states of the traffic lights ahead. Starting from a non-optimal implementation of the overall architecture, MTLA is further improved through a non-linear MPC approach. The developed system is tested in a virtual environment in IPG CarMaker and results show good performances with a high potential and space for future developments.
Robust Control For Spacecraft Attitude Tracking Under Multiple Physical Limitations with Guaranteed Performance
Authors: Jiakun Lei, Tao Meng, Weijia Wang, Chengjin Yin, Zhonghe Jin
Date: 2022-09-13 00:00:00

This paper considers the prescribed performance control (PPC) of spacecraft attitude tracking under multiple physical constraints, focusing on the robust issues. A novel Barrier Lyapunov function is proposed to realize the guaranteed-performance control under angular velocity constraint without singularity. Additionally, an adaptive strategy for the performance function is presented to soften the constraint and quickly re-stabilize the system after severe disturbances occur, providing strong robustness. Further, an auxiliary system is designed to handle the input saturation issue, incorporating the actuator limitation into the system. Based on the proposed structure, a backstepping controller is developed accordingly using a double-layer PPC framework. Numerical simulation results are presented to validate the proposed controller framework's efficiency and robustness.8 pa