pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Authors: Sharon Levy, Neha Anna John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
Date: 2023-05-18 00:00:00
Tasks: Attribute, Fairness, Sentiment Analysis

Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across various languages for individual attributes. As a result, it is critical to examine biases within each language and attribute. Of equal importance is to study how these biases compare across languages and how the biases are affected when training a model on multilingual data versus monolingual data. We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task to observe whether specific demographics are viewed more positively. We study bias similarities and differences across these languages and investigate the impact of multilingual vs. monolingual training data. We adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality, and gender. Our results reveal similarities in bias expression such as favoritism of groups that are dominant in each language's culture (e.g. majority religions and nationalities). Additionally, we find an increased variation in predictions across protected groups, indicating bias amplification, after multilingual finetuning in comparison to multilingual pretraining.
Recent Trends in Unsupervised Summarization
Authors: Mohammad Khosravani, Amine Trabelsi
Date: 2023-05-18 00:00:00
Tasks: Survey

Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets. This survey covers different recent techniques and models used for unsupervised summarization. We cover extractive, abstractive, and hybrid models and strategies used to achieve unsupervised summarization. While the main focus of this survey is on recent research, we also cover some of the important previous research. We additionally introduce a taxonomy, classifying different research based on their approach to unsupervised training. Finally, we discuss the current approaches and mention some datasets and evaluation methods.
Dive into the Power of Neuronal Heterogeneity
Authors: Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Yi Zeng
Date: 2023-05-19 00:00:00
Tasks: continuous-control, Continuous Control

The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of connections through training while modeling neurons as highly homogenized entities and lacking exploration of neural heterogeneity. Only a few studies have addressed neural heterogeneity by optimizing neuronal properties and connection weights to ensure network performance. However, this strategy impact the specific contribution of neuronal heterogeneity. In this paper, we first demonstrate the challenges faced by backpropagation-based methods in optimizing Spiking Neural Networks (SNNs) and achieve more robust optimization of heterogeneous neurons in random networks using an Evolutionary Strategy (ES). Experiments on tasks such as working memory, continuous control, and image recognition show that neuronal heterogeneity can improve performance, particularly in long sequence tasks. Moreover, we find that membrane time constants play a crucial role in neural heterogeneity, and their distribution is similar to that observed in biological experiments. Therefore, we believe that the neglected neuronal heterogeneity plays an essential role, providing new approaches for exploring neural heterogeneity in biology and new ways for designing more biologically plausible neural networks.
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments
Authors: Adel Nikfarjam, Ralf Rothenberger, Frank Neumann, Tobias Friedrich
Date: 2023-05-19 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satisfiability problem (SAT) in the context of EDO. SAT is of great importance in computer science and differs from the other problems studied in EDO literature, such as KP and TSP. SAT is heavily constrained, and the conventional evolutionary operators are inefficient in generating SAT solutions. Our approach avails of the following characteristics of SAT: 1) the possibility of adding more constraints (clauses) to the problem to forbid solutions or to fix variables, and 2) powerful solvers in the literature, such as minisat. We utilise such a solver to construct a diverse set of solutions. Moreover, maximising diversity provides us with invaluable information about the solution space of a given SAT problem, such as how large the feasible region is. In this study, we introduce evolutionary algorithms (EAs) employing a well-known SAT solver to maximise diversity among a set of SAT solutions explicitly. The experimental investigations indicate the introduced algorithms' capability to maximise diversity among the SAT solutions.
Accelerating Convergence in Global Non-Convex Optimization with Reversible Diffusion
Authors: Ryo Fujino
Date: 2023-05-19 00:00:00

Langevin Dynamics has been extensively employed in global non-convex optimization due to the concentration of its stationary distribution around the global minimum of the potential function at low temperatures. In this paper, we propose to utilize a more comprehensive class of stochastic processes, known as reversible diffusion, and apply the Euler-Maruyama discretization for global non-convex optimization. We design the diffusion coefficient to be larger when distant from the optimum and smaller when near, thus enabling accelerated convergence while regulating discretization error, a strategy inspired by landscape modifications. Our proposed method can also be seen as a time change of Langevin Dynamics, and we prove convergence with respect to KL divergence, investigating the trade-off between convergence speed and discretization error. The efficacy of our proposed method is demonstrated through numerical experiments.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Authors: Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, JiaQi Zhang, Fajie Yuan
Date: 2023-05-19 00:00:00
Tasks: Collaborative Filtering, News Recommendation, Recommendation Systems

Text-based collaborative filtering (TCF) has become the mainstream approach for text and news recommendation, utilizing text encoders, also known as language models (LMs), to represent items. However, existing TCF models primarily focus on using small or medium-sized LMs. It remains uncertain what impact replacing the item encoder with one of the largest and most powerful LMs, such as the 175-billion parameter GPT-3 model, would have on recommendation performance. Can we expect unprecedented results? To this end, we conduct an extensive series of experiments aimed at exploring the performance limits of the TCF paradigm. Specifically, we increase the size of item encoders from one hundred million to one hundred billion to reveal the scaling limits of the TCF paradigm. We then examine whether these extremely large LMs could enable a universal item representation for the recommendation task. Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm. Finally, we compare TCF with the recently popularized prompt-based recommendation using ChatGPT. Our research findings have not only yielded positive results but also uncovered some surprising and previously unknown negative outcomes, which can inspire deeper reflection and innovative thinking regarding text-based recommender systems. Codes and datasets will be released for further research.
The Barriers to Online Clothing Websites for Visually Impaired People: An Interview and Observation Approach to Understanding Needs
Authors: Amnah Alluqmani, Morgan Harvey, Ziqi Zhang
Date: 2023-05-19 00:00:00

Visually impaired (VI) people often face challenges when performing everyday tasks and identify shopping for clothes as one of the most challenging. Many engage in online shopping, which eliminates some challenges of physical shopping. However, clothes shopping online suffers from many other limitations and barriers. More research is needed to address these challenges, and extant works often base their findings on interviews alone, providing only subjective, recall-biased information. We conducted two complementary studies using both observational and interview approaches to fill a gap in understanding about VI people's behaviour when selecting and purchasing clothes online. Our findings show that shopping websites suffer from inaccurate, misleading, and contradictory clothing descriptions; that VI people mainly rely on (unreliable) search tools and check product descriptions by reviewing customer comments. Our findings also indicate that VI people are hesitant to accept assistance from automated, but that trust in such systems could be improved if researchers can develop systems that better accommodate users' needs and preferences.
BAT: Boundary aware transducer for memory-efficient and low-latency ASR
Authors: Keyu An, Xian Shi, Shiliang Zhang
Date: 2023-05-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Recognition

Recently, recurrent neural network transducer (RNN-T) gains increasing popularity due to its natural streaming capability as well as superior performance. Nevertheless, RNN-T training requires large time and computation resources as RNN-T loss calculation is slow and consumes a lot of memory. Another limitation of RNN-T is that it tends to access more contexts for better performance, thus leading to higher emission latency in streaming ASR. In this paper we propose boundary-aware transducer (BAT) for memory-efficient and low-latency ASR. In BAT, the lattice for RNN-T loss computation is reduced to a restricted region selected by the alignment from continuous integrate-and-fire (CIF), which is jointly optimized with the RNN-T model. Extensive experiments demonstrate that compared to RNN-T, BAT reduces time and memory consumption significantly in training, and achieves good CER-latency trade-offs in inference for streaming ASR.
Microservice Deployment in Edge Computing Based on Deep Q Learning
Authors: Wenkai Lv, Quan Wang, Pengfei Yang
Date: 2022-02-11 00:00:00
Proceeding: IEEE Transactions on Parallel and Distributed Systems 2022 2
Tasks: Edge-computing, Q-Learning

The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure
MAMIQA: No-Reference Image Quality Assessment Based on Multiscale Attention Mechanism With Natural Scene Statistics
Authors: Li Yu, Junyang Li, Farhad Pakdaman, Miaogen Ling, Moncef Gabbouj
Date: 2023-05-16 00:00:00
Proceeding: IEEE Signal Processing Letters 2023 5
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an image, according to human perception. Many recent studies use Transformers to assign different self-attention mechanisms to distinguish regions of an image, simulating the perception of the human visual system (HVS). However, the quadratic computational complexity caused by the self-attention mechanism is time-consuming and expensive. Meanwhile, the image resizing in the feature extraction stage loses the full-size image quality. To address these issues, we propose a lightweight attention mechanism using decomposed large-kernel convolutions to extract multiscale features, and a novel feature enhancement module to simulate HVS. We also propose to compensate the information loss caused by image resizing, with supplementary features from natural scene statistics. Experimental results on five standard datasets show that the proposed method surpasses the SOTA, while significantly reducing the computational costs.
Boosting Knowledge Graph Generation from Tabular Data with RML Views
Authors: Julián Arenas-Guerrero, Ahmad Alobaid, María Navas-Loro, María S. Pérez, Oscar Corcho
Date: 2023-05-22 00:00:00
Proceeding: Extended Semantic Web Conference 2023 5
Tasks: Data Integration, Graph Generation, Knowledge Graphs, Knowledge Graphs Data Curation

A large amount of data is available in tabular form. RML is commonly used to declare how such data can be transformed into RDF. However, RML presents limitations that lead, in many cases, to the need for additional preprocessing using scripting. Although some proposed extensions (e.g., FnO or RML fields) address some of these limitations, they are verbose, unfamiliar to most data engineers, and implemented in systems that do not scale up when large volumes of data need to be processed. In this work, we expand RML views to tabular sources so as to address the limitations of this mapping language. In this way, transformation functions, complex joins, or mixed syntax can be defined directly in SQL queries. We present our extension of Morph-KGC to efficiently support RML views for tabular sources. We validate our implementation adapting R2RML test cases with views and compare it against state-of-the-art RML+FnO systems showing that our system is significantly more scalable. Moreover, we present specific examples of a real use case in the public procurement domain where basic RML mappings could not be used without additional preprocessing.
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study
Authors: Yifan Tang, M. Rahmani Dehaghani, G. Gary Wang
Date: 2023-05-17 00:00:00
Tasks: Transfer Learning

Transfer learning (TL) based additive manufacturing (AM) modeling is an emerging field to reuse the data from historical products and mitigate the data insufficiency in modeling new products. Although some trials have been conducted recently, the inherent challenges of applying TL in AM modeling are seldom discussed, e.g., which source domain to use, how much target data is needed, and whether to apply data preprocessing techniques. This paper aims to answer those questions through a case study defined based on an open-source dataset about metal AM products. In the case study, five TL methods are integrated with decision tree regression (DTR) and artificial neural network (ANN) to construct six TL-based models, whose performances are then compared with the baseline DTR and ANN in a proposed validation framework. The comparisons are used to quantify the performance of applied TL methods and are discussed from the perspective of similarity, training data size, and data preprocessing. Finally, the source AM domain with larger qualitative similarity and a certain range of target-to-source training data size ratio are recommended. Besides, the data preprocessing should be performed carefully to balance the modeling performance and the performance improvement due to TL.
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Authors: Long Chen, Yuchen Li, Chao Huang, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Date: 2023-05-12 00:00:00
Tasks: Articles, Autonomous Driving, Ethics, Survey

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
Are Microphone Signals Alone Sufficient for Self-Positioning?
Authors: Faxian Cao, Yongqiang Cheng, Adil Mehmood Khan, Zhijing Yang
Date: 2023-05-19 00:00:00

In an era where asynchronous environments pose challenges to traditional self-positioning methods, we propose a new transformation to the existing paradigm. Traditionally, time of arrival (TOA) measurements require both microphone and source signals, limiting their applicability in environments with unknown emission time of human voices or sources and unknown recording start time of independent microphones. To address this issue, our research pioneers a mapping function capable of transforming both TOA and time difference of arrival (TDOA) formulas, demonstrating, for the first time, that they can be identical to one another. This implies that microphone signals alone are sufficient for self-positioning without the need for source signal waveforms, a groundbreaking advancement in the field that carries the potential to revolutionize self-positioning techniques, expanding their applicability in challenging environments. Supported by a robust mathematical proof and compelling experimental results, this research represents a timely and significant contribution to the current discourse in signal, and audio processing.
Adaptive identification of SISO linear infinite-dimensional systems
Authors: Sudipta Chattopadhyay, Srikant Sukumar, Vivek Natarajan
Date: 2023-05-19 00:00:00

We propose an adaptive algorithm for identifying the unknown parameter in a linear exponentially stable single-input single-output infinite-dimensional system. We assume that the transfer function of the infinite-dimensional system can be expressed as a ratio of two infinite series in s (the Laplace variable). We also assume that certain identifiability conditions, which include a persistency of excitation condition, hold. For a fixed integer n, we propose an update law driven by real-time input-output data for estimating the first n+1 coefficients in the numerator and the denominator of the transfer function. We show that the estimates for the transfer function coefficients generated by the update law are close to the true values at large times provided n is sufficiently large (the estimates converge to the true values as time and n tend to infinity). The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter. We also provide a numerical scheme for verifying the identifiability conditions and for choosing n sufficiently large so that the value of the reconstructed parameter is close to the true value. The class of systems to which our approach is applicable includes many partial differential equations with constant/spatially-varying coefficients and distributed/boundary input and output. We illustrate the efficacy of our approach using three examples: a delay system with four unknown scalars, a 1D heat equation with two unknown scalars and a 1D wave equation with an unknown spatially-varying coefficient.
Information Technology Needs in Vehicle Dynamics Control
Authors: Levent Guvenc, Bilin Aksun-Guvenc
Date: 2023-05-19 00:00:00

Significant changes are occurring in the field of vehicle dynamics control. Consequently, vehicle dynamics control systems are expected to be as common as ABS systems in the near future. This paper focuses on the information technology related requirements posed by these advances in the area of vehicle dynamics control. Topics considered include vehicle dynamics simulation tools, hardware in the loop simulation systems, sensors and related fault tolerance/diagnostics, man machine interface problems and the implications of a networked architecture on controller design.
Quasi-static closed-loop wind-farm control for combined power and fatigue optimization
Authors: Ishaan Sood, Christophe del Fosse et d'Espierres, Johan Meyers
Date: 2023-05-19 00:00:00

We develop a methodology for combined power and loads optimization by coupling a surrogate loads model with an analytical quasi-static Gaussian wake merging model. The look-up table based fatigue model is developed offline through a series of OpenFast simulations, covering different operational states of a DTU 10 MW reference wind turbine, and verified against large eddy simulations with aeroelastic coupling. Subsequently, optimal control set points for the TotalControl reference wind power plant are obtained using the analytical model, and tested in a wind farm emulator that is based on large eddy simulations. The wake model is calibrated online using in a quasi-static closed-loop manner. Benefits of the closed-loop controller are exhibited via comparison of farm performance against greedy operation and against open-loop control results obtained without feedback or calibration. Results show that the closed-loop control out performs open-loop control, with farm configurations with deep turbine arrays showcasing the highest gains. Inclusion of fatigue in the cost function through the developed LUT also leads to interesting insights, with reduced blade root fatigue loading without significant decrease in power production when compared to open-loop control. A case study is also performed which showcases real world applications for the developed closed-loop controller and the load LUT, in which the closed-loop controller is shown to react to scenarios with turbine operation shut-down, optimizing the yaw angles online to maximize performance for the new layout.