pwc-archive/papers-with-abstracts huggingface dataset:
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
Authors: Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, Xing Xie
Date: 2022-05-22 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item candidates proposed by recall modules. With the success of deep learning techniques in various domains, we have witnessed the mainstream rankers evolve from traditional models to deep neural models. However, the way that we design and use rankers remains unchanged: offline training the model, freezing the parameters, and deploying it for online serving. Actually, the candidate items are determined by specific user requests, in which underlying distributions (e.g., the proportion of items for different categories, the proportion of popular or new items) are highly different from one another in a production environment. The classical parameter-frozen inference manner cannot adapt to dynamic serving circumstances, making rankers' performance compromised. In this paper, we propose a new training and inference paradigm, termed as Ada-Ranker, to address the challenges of dynamic online serving. Instead of using parameter-frozen models for universal serving, Ada-Ranker can adaptively modulate parameters of a ranker according to the data distribution of the current group of item candidates. We first extract distribution patterns from the item candidates. Then, we modulate the ranker by the patterns to make the ranker adapt to the current data distribution. Finally, we use the revised ranker to score the candidate list. In this way, we empower the ranker with the capacity of adapting from a global model to a local model which better handles the current task.
Micro-video recommendation model based on graph neural network and attention mechanism
Authors: Chan Ching Ting, Mathew Bowles, Ibrahim Idewu
Date: 2022-05-21 00:00:00
Tasks: Graph Neural Network

With the rapid development of Internet technology and the comprehensive popularity of Internet applications, online activities have gradually become an indispensable part of people's daily life. The original recommendation learning algorithm is mainly based on user-microvideo interaction for learning, modeling the user-micro-video connection relationship, which is difficult to capture the more complex relationships between nodes. To address the above problems, we propose a personalized recommendation model based on graph neural network, which utilizes the feature that graph neural network can tap deep information of graph data more effectively, and transforms the input user rating information and item side information into graph structure, for effective feature extraction, based on the importance sampling strategy. The importance-based sampling strategy measures the importance of neighbor nodes to the central node by calculating the relationship tightness between the neighbor nodes and the central node, and selects the neighbor nodes for recommendation tasks based on the importance level, which can be more targeted to select the sampling neighbors with more influence on the target micro-video nodes. The pooling aggregation strategy, on the other hand, trains the aggregation weights by inputting the neighborhood node features into the fully connected layer before aggregating the neighborhood features, and then introduces the pooling layer for feature aggregation, and finally aggregates the obtained neighborhood aggregation features with the target node itself, which directly introduces a symmetric trainable function to fuse the neighborhood weight training into the model to better capture the different neighborhood nodes' differential features in a learnable manner to allow for a more accurate representation of the current node features.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Authors: Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Kaixin Gao, Bing Han, Lin Zheng, Xiaobo Guo
Date: 2022-05-16 00:00:00
Tasks: Graph Neural Network, Graph Representation Learning, Representation Learning, Sequential Recommendation

Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of SR generally present the intrinsic power-law distribution, which can be ascended to hierarchy-like structures. Previous methods usually handle such hierarchical information by making user-item sectionalization empirically under Euclidean space, which may cause distortion of user-item representation in real online scenarios. In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously. Specifically, for the purpose of explicitly capturing the hierarchical information, we first construct a weighted user-item heterogeneous graph by aliening all the user-item interactions to improve the perception domain of each user from a global view. Then the output of the global representation would be used to complement the local directed item-item homogeneous graph convolution. By defining a novel hyperbolic inner product operator, the global and local graph representation learning are directly conducted in Poincar\'{e} ball instead of commonly used projection operation between Poincar\'{e} ball and Euclidean space, which could alleviate the cumulative error issue of general bidirectional translation process. Moreover, for the purpose of explicitly capturing the sequential dependency information, we design two types of temporal attention operations under Poincar\'{e} ball space. Empirical evaluations on datasets from the public and financial industry show that PHGR outperforms several comparison methods.
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Authors: Zhenning Zhang, Boxin Du, Hanghang Tong
Date: 2022-05-05 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied in this problem and achieve superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SUGER, for bundle recommendation to handle these limitations. SUGER generates heterogeneous subgraphs around the user-bundle pairs, and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in both the basic and the transfer bundle recommendation problems.
Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendations
Authors: Dinuka Ravijaya Piyadigama, Guhanathan Poravi
Date: 2022-05-03 00:00:00
Tasks: Information Retrieval, Opinion Mining, Retrieval

Recommendations Systems have been identified to be one of the integral elements of driving sales in e-commerce sites. The utilization of opinion mining data extracted from trends has been attempted to improve the recommendations that can be provided by baseline methods in this research when user-click data is lacking or is difficult to be collected due to privacy concerns. Utilizing social trends to influence the recommendations generated for a set of unique items has been explored with the use of a suggested scoring mechanism. Embracing concepts from decentralized networks that are expected to change how users interact via the internet over the next couple of decades, the suggested Recommendations System attempts to make use of multiple sources of information, applying coherent information retrieval techniques to extract probable trending items. The proposed Recommendations Architecture in the research presents a method to integrate social trends with recommendations to produce promising outputs.
[Re] Distilling Knowledge via Knowledge Review
Authors: Apoorva Verma, Pranjal Gulati, Sarthak Gupta
Date: 2022-05-18 00:00:00

This effort aims to reproduce the results of experiments and analyze the robustness of the review framework for knowledge distillation introduced in the CVPR '21 paper 'Distilling Knowledge via Knowledge Review' by Chen et al. Previous works in knowledge distillation only studied connections paths between the same levels of the student and the teacher, and cross-level connection paths had not been considered. Chen et al. propose a new residual learning framework to train a single student layer using multiple teacher layers. They also design a novel fusion module to condense feature maps across levels and a loss function to compare feature information stored across different levels to improve performance. In this work, we consistently verify the improvements in test accuracy across student models as reported in the original paper and study the effectiveness of the novel modules introduced by conducting ablation studies and new experiments.
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
Authors: Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Date: 2022-05-17 00:00:00
Tasks: Quantization

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in significant degradation of CNN model accuracy. In contrast, heterogeneous quantization represents a promising approach to realize compact, quantized models with higher inference accuracies. In this paper, we propose HQNNA, a CNN accelerator based on non-coherent silicon photonics that can accelerate both homogeneously quantized and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves up to 73.8x better energy-per-bit and 159.5x better throughput-energy efficiency than state-of-the-art photonic CNN accelerators.
The Topology of Circular Synthetic Aperture Sonar Targets
Authors: Michael Robinson, Zander Memon
Date: 2022-05-19 00:00:00
Tasks: Object

This report presents a connection between the physical acoustics of an object and the topology of the space of echoes resulting from a circular synthetic aperture sonar (CSAS) collection of that object. A simple theoretical model is developed that yields a precise, yet qualitative, description of the space of echoes. This theoretical model is validated in simulation and with experimental data from a laboratory sonar system.
Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection
Authors: Anselmo Ferreira, Changcheng Chen, Mauro Barni
Date: 2022-05-16 00:00:00

Nowadays, 2D barcodes have been widely used for advertisement, mobile payment, and product authentication. However, in applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. In this paper, we employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (rebroadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process. The proposed features are composed of global and local descriptors, which characterize the multi-scale texture appearance and the points of interest distribution, respectively. The proposed descriptors are compared against some existing texture descriptors and deep learning-based approaches under various scenarios, such as cross-datasets and cross-size. Experimental results highlight the practicality of the proposed method in real-world settings.
Exploiting Array Geometry for Reduced-Subspace Channel Estimation in RIS-Aided Communications
Authors: Özlem Tuğfe Demir, Emil Björnson, Luca Sanguinetti
Date: 2022-05-23 00:00:00

A reconfigurable intelligent surface (RIS) can be used to improve the channel gain between a base station (BS) and user equipment (UE), but only if its $N$ reflecting elements are configured properly. This requires accurate estimation of the cascaded channel from the UE to the BS through each RIS element. If the channel structure is not exploited, pilot sequences of length $N$ must be used, which is a major practical challenge since $N$ is typically at the order of hundreds. To address this problem without requiring user-specific channel statistics, we propose a novel estimator, called reduced-subspace least squares (RS-LS) estimator, that only uses knowledge of the array geometry. The RIS phase-shift pattern is optimized to minimize the mean-square error of the channel estimates. The RS-LS estimator largely outperforms the conventional least-squares estimator, and can be utilized with a much shorter pilot length since it exploits the fact that the array geometry confines the possible channel realizations to a reduced-rank subspace.
Approximate Message Passing with Parameter Estimation for Heavily Quantized Measurements
Authors: Shuai Huang, Deqiang Qiu, Trac D. Tran
Date: 2022-05-20 00:00:00
Tasks: Computational Efficiency, parameter estimation, Quantization

Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approximate message passing (AMP) framework to achieve this goal given its high computational efficiency and state-of-the-art performance. In AMP, the signal of interest is assumed to follow certain prior distribution with unknown parameters. Previous works focused on finding the parameters that maximize the measurement likelihood via expectation maximization -- an increasingly difficult problem to solve in cases involving complicated probability models. In this paper, we treat the parameters as unknown variables and compute their posteriors via AMP. The parameters and signal of interest can then be jointly recovered. Compared to previous methods, the proposed approach leads to a simple and elegant parameter estimation scheme, allowing us to directly work with 1-bit quantization noise model. We then further extend our approach to general multi-bit quantization noise model. Experimental results show that the proposed framework provides significant improvement over state-of-the-art methods across a wide range of sparsity and noise levels.
Deep Neural Network approaches for Analysing Videos of Music Performances
Authors: Foteini Simistira Liwicki, Richa Upadhyay, Prakash Chandra Chhipa, Killian Murphy, Federico Visi, Stefan Östersjö, Marcus Liwicki
Date: 2022-05-05 00:00:00

This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and make learning possible for co-existent gestures by batch balancing approach and spatial-temporal representations of gestures. (ii) Performs a detailed study on 7 and 18 categories of gestures generated during the performance (guitar play) of musical pieces that have been video-recorded. (iii) Investigates the possibility to use audio features. (iv) Extends the analysis to multiple videos. The novel methods significantly improve the performance of gesture identification by 12 %, when compared to the previous work (51 % in this study over 39 % in previous work). We successfully validate the proposed methods on 7 super classes (72 %), an ensemble of the 18 gestures/classes, and additional videos (75 %).
SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks
Authors: Nicolas Weber
Date: 2022-05-19 00:00:00
Tasks: CPU, GPU

The increased interest in Artificial Intelligence (AI) raised the need for highly optimized and sophisticated AI frameworks. Starting with the Lua-based Torch many frameworks have emerged over time, such as Theano, Caffe, Chainer, CNTK, MxNet, PyTorch, DL4J, or TensorFlow. All of these provide a high level scripting API that allows users to easily design neural networks and run these on various kinds of hardware. What the user usually does not see is the high effort put into these frameworks to provide peak execution performance. While mainstream CPUs and GPUs have the "luxury" to have a wide spread user base in the open source community, less mainstream CPU, GPU or accelerator vendors need to put in a high effort to get their hardware supported by these frameworks. This includes not only the development of highly efficient compute libraries such as CUDNN, OneDNN or VEDNN but also supporting an ever growing number of simpler compute operations such as summation and multiplications. Each of these frameworks, nowadays, supports several hundred of unique operations, with tensors of various sizes, shapes and data types, which end up in thousands of compute kernels required for each device type. And the number of operations keeps increasing. That is why NEC Laboratories Europe started developing the SOL AI Optimization project already years ago, to deliver optimal performance to users while keeping the maintenance burden minimal.
Model Predictive Control of Non-Holonomic Vehicles: Beyond Differential-Drive
Authors: Mario Rosenfelder, Henrik Ebel, Jasmin Krauspenhaar, Peter Eberhard
Date: 2022-05-23 00:00:00
Tasks: Model Predictive Control

Non-holonomic vehicles are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking, is known to be challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system approximations, this paper proposes a comprehensive, ready-to-apply design procedure for MPC controllers to steer controllable, driftless non-holonomic vehicles into given setpoints. It can be ascertained that the resulting controllers nominally asymptotically stabilize the setpoint for a large-enough prediction horizon. The design procedure is exemplarily applied to four vehicles, including the kinematic car and a differentially driven mobile robot with up to two trailers. The controllers use a non-quadratic cost function tailored to the non-holonomic kinematics. Novelly, for the considered example vehicles, it is proven that a quadratic cost employed in an otherwise similar controller is insufficient to reliably asymptotically stabilize the closed loop. Since quadratic costs are the conventional choice in control, this highlights the relevance of the findings. To the knowledge of the authors, it is the first time that MPC controllers of the proposed structure are applied to non-holonomic vehicles beyond very simple ones, in particular (partly) on hardware.
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Authors: Meng Yu, Yong Xu, Chunlei Zhang, Shi-Xiong Zhang, Dong Yu
Date: 2022-05-20 00:00:00
Tasks: Acoustic echo cancellation, Speech Enhancement, speech-recognition, Speech Recognition

Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when the loudspeaker plays back. In this paper, we present an all-deep-learning framework that implicitly estimates the second order statistics of echo/noise and target speech, and jointly solves echo and noise suppression through an attention based recurrent neural network. The proposed model outperforms the state-of-the-art joint echo cancellation and speech enhancement method F-T-LSTM in terms of objective speech quality metrics, speech recognition accuracy and model complexity. We show that this model can work with speaker embedding for better target speech enhancement and furthermore develop a branch for automatic gain control (AGC) task to form an all-in-one front-end speech enhancement system.
Fractional-Order Partial Cancellation of Integer-Order Poles and Zeros
Authors: Benjamin Voß, Christoph Weise, Michael Ruderman, Johann Reger
Date: 2022-05-23 00:00:00

The key idea of this contribution is the partial compensation of non-minimum phase zeros or unstable poles. Therefore the integer-order zero/pole is split into a product of fractional-order pseudo zeros/poles. The amplitude and phase response of these fractional-order terms is derived to include these compensators into the loop-shaping design. Such compensators can be generalized to conjugate complex zeros/poles, and also implicit fractional-order terms can be applied. In the case of the non-minimum phase zero, its compensation leads to a higher phase margin and a steeper open-loop amplitude response around the crossover frequency resulting in a reduced undershooting in the step-response, as illustrated in the numerical example.
A mode-in-state contribution factor based on Koopman operator and its application to power system analysis
Authors: Kenji Takamichi, Yoshihiko Susuki, Marcos Netto, Atsushi Ishigame
Date: 2022-05-23 00:00:00
Tasks: Sensitivity

This paper proposes a mode-in-state contribution factor for a class of nonlinear dynamical systems by utilizing spectral properties of the Koopman operator and sensitivity analysis. Using eigenfunctions of the Koopman operator for a target nonlinear system, we show that the relative contribution between modes and state variables can be quantified beyond a linear regime, where the nonlinearity of the system is taken into consideration. The proposed contribution factor is applied to the numerical analysis of large-signal simulations for an interconnected AC/multi-terminal DC power system.
Data-driven, Internet-inspired, and Scalable EV Charging for Power Distribution Grid
Authors: Emin Ucer, Mithat Kisacikoglu, Murat Yuksel, Ali C. Gurbuz
Date: 2022-05-22 00:00:00
Tasks: Fairness

Electric vehicles (EVs) are finally making their way onto the roads. However, the challenges concerning their long charging times and their impact on congestion of the power distribution grid are still waiting to be resolved. With historical measurement data, EV chargers can take better-informed actions while staying mostly off-line. Proposed solutions that depend on heavy communication and rigorous computation for optimal operation are not scalable. The solutions that do not depend on power distribution topology information, such as Droop control, are more practical as they only use local measurements. However, they result in sub-optimal operation due to a lack of a feedback mechanism. This study develops a distributed and data-driven congestion detection methodology embedded in the Additive Increase Multiplicative Decrease (AIMD) algorithm to control mass EV charging in a distribution grid. The proposed distributed AIMD algorithm performs very closely to the ideal AIMD regarding fairness and congestion handling. Its communication need is almost as low as the Droop control. The results can provide crucial insights on how we can use data to reveal the inner dynamics and structure of the power grid and help develop more advanced data-driven algorithms for grid-integrated power electronics control.