pwc-archive/papers-with-abstracts huggingface dataset:
Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
Authors: Hyunsik Jeon, Jun-Gi Jang, Taehun Kim, U Kang
Date: 2022-10-19 00:00:00
Tasks: Multi-Task Learning

How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3x higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles.
Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching
Authors: Huiran Li, Yanwu Yang
Date: 2022-10-19 00:00:00
Tasks: Management

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Authors: Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang, Shaoliang Nie
Date: 2022-10-14 00:00:00
Tasks: counterfactual, Counterfactual Inference, Explainable Recommendation, Explanation Generation, Fairness, Text Generation

As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attributes. The model can inherit the bias and perpetuate inequality in generating text w.r.t. users' protected attributes, leading to unfair treatment when serving users. In this work, we investigate fairness of PTG in the context of personalized explanation generation for recommendations. We first discuss the biases in generated explanations and their fairness implications. To promote fairness, we introduce a general framework to achieve measure-specific counterfactual fairness in explanation generation. Extensive experiments and human evaluations demonstrate the effectiveness of our method.
Cross-Domain Neural Entity Linking
Authors: Hassan Soliman
Date: 2022-09-28 00:00:00
Tasks: Entity Linking

Entity Linking is the task of matching a mention to an entity in a given knowledge base (KB). It contributes to annotating a massive amount of documents existing on the Web to harness new facts about their matched entities. However, existing Entity Linking systems focus on developing models that are typically domain-dependent and robust only to a particular knowledge base on which they have been trained. The performance is not as adequate when being evaluated on documents and knowledge bases from different domains. Approaches based on pre-trained language models, such as Wu et al. (2020), attempt to solve the problem using a zero-shot setup, illustrating some potential when evaluated on a general-domain KB. Nevertheless, the performance is not equivalent when evaluated on a domain-specific KB. To allow for more accurate Entity Linking across different domains, we propose our framework: Cross-Domain Neural Entity Linking (CDNEL). Our objective is to have a single system that enables simultaneous linking to both the general-domain KB and the domain-specific KB. CDNEL works by learning a joint representation space for these knowledge bases from different domains. It is evaluated using the external Entity Linking dataset (Zeshel) constructed by Logeswaran et al. (2019) and the Reddit dataset collected by Botzer et al. (2021), to compare our proposed method with the state-of-the-art results. The proposed framework uses different types of datasets for fine-tuning, resulting in different model variants of CDNEL. When evaluated on four domains included in the Zeshel dataset, these variants achieve an average precision gain of 9%.
Vector Diffusion Maps and the Connection Laplacian
Authors: Amit Singer, Hau-Tieng Wu
Date: 2011-02-01 00:00:00
Tasks: Dimensionality Reduction

We introduce {\em vector diffusion maps} (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other non-linear dimensionality reduction methods, such as LLE, ISOMAP and Laplacian eigenmaps. While existing methods are either directly or indirectly related to the heat kernel for functions over the data, VDM is based on the heat kernel for vector fields. VDM provides tools for organizing complex data sets, embedding them in a low dimensional space, and interpolating and regressing vector fields over the data. In particular, it equips the data with a metric, which we refer to as the {\em vector diffusion distance}. In the manifold learning setup, where the data set is distributed on (or near) a low dimensional manifold $\MM^d$ embedded in $\RR^{p}$, we prove the relation between VDM and the connection-Laplacian operator for vector fields over the manifold.
An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits
Authors: Udit Kumar Agarwal, Shikhar Makhija, Varun Tripathi, Kunwar Singh
Date: 2022-08-19 00:00:00

The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find great application in neuromorphic circuits as it is possible to couple memory and processing, compared to traditional Von-Neumann digital architectures where memory and processing are separate. Neural networks have a layered structure where information passes from one layer to another and each of these layers have the possibility of a high degree of parallelism. CMOS-Memristor based neural network accelerators provide a method of speeding up neural networks by making use of this parallelism and analog computation. In this project we have conducted an initial investigation into the current state of the art implementation of memristor based programming circuits. Various memristor programming circuits and basic neuromorphic circuits have been simulated. The next phase of our project revolved around designing basic building blocks which can be used to design neural networks. A memristor bridge based synaptic weighting block, a operational transconductor based summing block were initially designed. We then designed activation function blocks which are used to introduce controlled non-linearity. Blocks for a basic rectified linear unit and a novel implementation for tan-hyperbolic function have been proposed. An artificial neural network has been designed using these blocks to validate and test their performance. We have also used these fundamental blocks to design basic layers of Convolutional Neural Networks. Convolutional Neural Networks are heavily used in image processing applications. The core convolutional block has been designed and it has been used as an image processing kernel to test its performance.
Privacy-preserving Automatic Speaker Diarization
Authors: Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso
Date: 2022-10-26 00:00:00
Tasks: Privacy Preserving, speaker-diarization, Speaker Diarization

Automatic Speaker Diarization (ASD) is an enabling technology with numerous applications, which deals with recordings of multiple speakers, raising special concerns in terms of privacy. In fact, in remote settings, where recordings are shared with a server, clients relinquish not only the privacy of their conversation, but also of all the information that can be inferred from their voices. However, to the best of our knowledge, the development of privacy-preserving ASD systems has been overlooked thus far. In this work, we tackle this problem using a combination of two cryptographic techniques, Secure Multiparty Computation (SMC) and Secure Modular Hashing, and apply them to the two main steps of a cascaded ASD system: speaker embedding extraction and agglomerative hierarchical clustering. Our system is able to achieve a reasonable trade-off between performance and efficiency, presenting real-time factors of 1.1 and 1.6, for two different SMC security settings.
The Art NFTs and Their Marketplaces
Authors: Lanqing Du, Michelle Kim, Jinwook Lee
Date: 2022-10-23 00:00:00

Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier for ownership, powered by blockchain technology. Technically speaking, anything digital could be minted and sold as an NFT, which provides proof of ownership and authenticity of a digital file. For this reason, it helps us distinguish between the originals and their copies, making it possible to trade them. This paper focuses on art NFTs that change how artists can sell their products. It also changes how the art trade market works since NFT technology cuts out the middleman. Recently, the utility of NFTs has become an essential issue in the NFT ecosystem, which refers to the owners' usefulness, profitability, and benefits. Using recent major art NFT marketplace datasets, we summarize and interpret the current market trends and patterns in a way that brings insight into the future art market. Numerical examples are presented.
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
Authors: Jiyang Guan, Jian Liang, Ran He
Date: 2022-10-21 00:00:00
Tasks: Adversarial Defense, Transfer Learning

An off-the-shelf model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting aims to verify whether a suspect model is stolen from the victim model, which gains more and more attention nowadays. Previous methods always leverage the transferable adversarial examples as the model fingerprint, which is sensitive to adversarial defense or transfer learning scenarios. To address this issue, we consider the pairwise relationship between samples instead and propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC). Specifically, we present SAC-w that selects wrongly classified normal samples as model inputs and calculates the mean correlation among their model outputs. To reduce the training time, we further develop SAC-m that selects CutMix Augmented samples as model inputs, without the need for training the surrogate models or generating adversarial examples. Extensive results validate that SAC successfully defends against various model stealing attacks, even including adversarial training or transfer learning, and detects the stolen models with the best performance in terms of AUC across different datasets and model architectures. The codes are available at https://github.com/guanjiyang/SAC.
DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
Authors: Rui Luo, Fei Liu, Wenhao Liang, Yuhong Zhang, Chenyang Bu, Xuegang Hu
Date: 2022-10-18 00:00:00
Tasks: Knowledge Tracing, Question Similarity

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT.
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
Authors: He Zhang, Sizhen Li, Liang Zhang, David H. Mathews, Liang Huang
Date: 2022-10-26 00:00:00

Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful. Some existing tools are less accurate due to omitting the competing of intermolecular and intramolecular base pairs, or focus more on predicting the binding region rather than predicting the complete secondary structure of two interacting strands. Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences. To address these issues, we present LinearCoFold, which predicts the complete minimum free energy structure of two strands in linear runtime, and LinearCoPartition, which calculates the cofolding partition function and base pairing probabilities in linear runtime. LinearCoFold and LinearCoPartition follows the concatenation strategy of RNAcofold, but are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8x faster than RNAcofold MFE mode (0.6 minutes vs. 52.1 minutes), and LinearCoPartition is 642.3x faster than RNAcofold partition function mode (1.8 minutes vs. 1156.2 minutes). Different from the local algorithms, LinearCoFold and LinearCoPartition are global cofolding algorithms without restriction on base pair length. Surprisingly, LinearCoFold and LinearCoPartition's predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has been experimentally studied, and observe that LinearCoFold's prediction correlates better to the wet lab results.
HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields
Authors: You Zhang, Yuxiang Wang, Zhiyao Duan
Date: 2022-10-27 00:00:00

Head-related transfer functions (HRTFs) are a set of functions describing the spatial filtering effect of the outer ear (i.e., torso, head, and pinnae) onto sound sources at different azimuth and elevation angles. They are widely used in spatial audio rendering. While the azimuth and elevation angles are intrinsically continuous, measured HRTFs in existing datasets employ different spatial sampling schemes, making it difficult to model HRTFs across datasets. In this work, we propose to use neural fields, a differentiable representation of functions through neural networks, to model HRTFs with arbitrary spatial sampling schemes. Such representation is unified across datasets with different spatial sampling schemes. HRTFs for arbitrary azimuth and elevation angles can be derived from this representation. We further introduce a generative model named HRTF field to learn the latent space of the HRTF neural fields across subjects. We demonstrate promising performance on HRTF interpolation and generation tasks and point out potential future work.
Model Order Selection with Variational Autoencoding
Authors: Michael Baur, Franz Weißer, Benedikt Böck, Wolfgang Utschick
Date: 2022-10-27 00:00:00
Tasks: Decoder, model

Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning-based methods are promising alternatives for such challenging situations as they compensate lack of information in the available observations with training on large datasets. This manuscript proposes an approach that uses a variational autoencoder (VAE) for model order selection. The idea is to learn a parameterized conditional covariance matrix at the VAE decoder that approximates the true signal covariance matrix. The method is unsupervised and only requires a small representative dataset for calibration after training the VAE. Numerical simulations show that the proposed method outperforms classical methods and even reaches or beats a supervised approach depending on the considered snapshots.
TPU-MLIR: A Compiler For TPU Using MLIR
Authors: Pengchao Hu, Man Lu, Lei Wang, Guoyue Jiang
Date: 2022-10-23 00:00:00

Multi-level intermediate representations (MLIR) show great promise for reducing the cost of building domain-specific compilers by providing a reusable and extensible compiler infrastructure. This work presents TPU-MLIR, an end-to-end compiler based on MLIR that deploys pre-trained neural network (NN) models to a custom ASIC called a Tensor Processing Unit (TPU). TPU-MLIR defines two new dialects to implement its functionality: 1. a Tensor operation (TOP) dialect that encodes the deep learning graph semantics and independent of the deep learning framework and 2. a TPU kernel dialect to provide a standard kernel computation on TPU. A NN model is translated to the TOP dialect and then lowered to the TPU dialect for different TPUs according to the chip's configuration. We demonstrate how to use the MLIR pass pipeline to organize and perform optimization on TPU to generate machine code. The paper also presents a verification procedure to ensure the correctness of each transform stage.
Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction
Authors: Annika Briegleb, Mhd Modar Halimeh, Walter Kellermann
Date: 2022-10-27 00:00:00
Tasks: Position, Target Speaker Extraction

In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial. In this contribution, we investigate the spatial filtering performed by such a time-varying spectro-spatial filter. We extend the recently proposed complex-valued spatial autoencoder (COSPA) for the task of target speaker extraction by leveraging its interpretable structure and purposefully informing the network of the target speaker's position. We show that the resulting informed COSPA (iCOSPA) effectively and flexibly extracts a target speaker from a mixture of speakers. We also find that the proposed architecture is well capable of learning pronounced spatial selectivity patterns and show that the results depend significantly on the training target and the reference signal when computing various evaluation metrics.
Time-Domain Based Embeddings for Spoofed Audio Representation
Authors: Matan Karo, Arie Yeredor, Itshak Lapidot
Date: 2022-10-27 00:00:00

Anti-spoofing is the task of speech authentication. That is, identifying genuine human speech compared to spoofed speech. The main focus of this paper is to suggest new representations for genuine and spoofed speech, based on the probability mass function (PMF) estimation of the audio waveforms' amplitude. We introduce a new feature extraction method for speech audio signals: unlike traditional methods, our method is based on direct processing of time-domain audio samples. The PMF is utilized by designing a feature extractor based on different PMF distances and similarity measures. As an additional step, we used filter-bank preprocessing, which significantly affects the discriminative characteristics of the features and facilitates convenient visualization of possible clustering of spoofing attacks. Furthermore, we use diffusion maps to reveal the underlying manifold on which the data lies. The suggested embeddings allow the use of simple linear separators to achieve decent performance. In addition, we present a convenient way to visualize the data, which helps to assess the efficiency of different spoofing techniques. The experimental results show the potential of using multi-channel PMF based features for the anti-spoofing task, in addition to the benefits of using diffusion maps both as an analysis tool and as an embedding tool.