pwc-archive/papers-with-abstracts huggingface dataset:
Repeated Padding for Sequential Recommendation
Authors: Yizhou Dang, YuTing Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao
Date: 2024-03-11 00:00:00
Tasks: Common Sense Reasoning, Data Augmentation, Sequential Recommendation

Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batching-based training needs to ensure that the sequences in each batch have the same length. The special value \emph{0} is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has never been explored before: \emph{Can we fully utilize this idle input space by padding other content to further improve model performance and training efficiency?} In this paper, we propose a simple yet effective padding method called \textbf{Rep}eated \textbf{Pad}ding (\textbf{RepPad}). Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training. This operation can be performed a finite number of times or repeated until the input sequences' length reaches the maximum limit. Our RepPad can be viewed as a sequence-level data augmentation strategy. Unlike most existing works, our method contains no trainable parameters or hyperparameters and is a plug-and-play data augmentation operation. Extensive experiments on various categories of sequential models and five real-world datasets demonstrate the effectiveness and efficiency of our approach. The average recommendation performance improvement is up to 60.3\% on GRU4Rec and 24.3\% on SASRec. We also provide in-depth analysis and explanation of what makes RepPad effective from multiple perspectives. Our datasets and codes are available at \url{https://github.com/KingGugu/RepPad}.
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
Authors: Yashar Deldjoo, Tommaso Di Noia
Date: 2024-03-08 00:00:00
Tasks: Attribute, Fairness, Language Modeling, Language Modelling, Large Language Model, Profile Generation, Recommendation Systems

This work takes a critical stance on previous studies concerning fairness evaluation in Large Language Model (LLM)-based recommender systems, which have primarily assessed consumer fairness by comparing recommendation lists generated with and without sensitive user attributes. Such approaches implicitly treat discrepancies in recommended items as biases, overlooking whether these changes might stem from genuine personalization aligned with the true preferences of users. Moreover, these earlier studies typically address single sensitive attributes in isolation, neglecting the complex interplay of intersectional identities. In response to these shortcomings, we introduce CFaiRLLM, an enhanced evaluation framework that not only incorporates true preference alignment but also rigorously examines intersectional fairness by considering overlapping sensitive attributes. Additionally, CFaiRLLM introduces diverse user profile sampling strategies-random, top-rated, and recency-focused-to better understand the impact of profile generation fed to LLMs in light of inherent token limitations in these systems. Given that fairness depends on accurately understanding users' tastes and preferences, these strategies provide a more realistic assessment of fairness within RecLLMs. To validate the efficacy of CFaiRLLM, we conducted extensive experiments using MovieLens and LastFM datasets, applying various sampling strategies and sensitive attribute configurations. The evaluation metrics include both item similarity measures and true preference alignment considering both hit and ranking (Jaccard Similarity and PRAG), thereby conducting a multifaceted analysis of recommendation fairness.
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners
Authors: Shanu Vashishtha, Abhinav Prakash, Lalitesh Morishetti, Kaushiki Nag, Yokila Arora, Sushant Kumar, Kannan Achan
Date: 2024-02-28 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Prompt Engineering

Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ a manual process to generate the banners, which is time-consuming and has limitations of scalability. In this work, we demonstrate the use of text-to-image models for generating personalized web banners with dynamic content for online shoppers based on their interactions. The novelty in this approach lies in converting users' interaction data to meaningful prompts without human intervention. To this end, we utilize a large language model (LLM) to systematically extract a tuple of attributes from item meta-information. The attributes are then passed to a text-to-image model via prompt engineering to generate images for the banner. Our results show that the proposed approach can create high-quality personalized banners for users.
Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach
Authors: Jiawei Tang, Yuxing Zhong, Pengyu Wang, Xingzhou Chen, Shuang Wu, Ling Shi
Date: 2024-03-10 00:00:00

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle {high-order} systems. This paper fills the research gap by considering the direct shooting method for {high-order} systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Authors: Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, Goce Trajcevski
Date: 2023-12-01 00:00:00
Proceeding: IEEE Transactions on Big Data 2023 12
Tasks: Fine-Grained Urban Flow Inference, Image Super-Resolution, Super-Resolution

Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: S patial- T emporal C ontrasting for Fine-Grained Urban F low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting spatial-temporally similar flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.
Asynchronous Microphone Array Calibration using Hybrid TDOA Information
Authors: Chengjie Zhang, Jiang Wang, He Kong
Date: 2024-03-09 00:00:00

Asynchronous microphone array calibration is a prerequisite for many audition robot applications. A popular solution to the above calibration problem is the batch form of Simultaneous Localisation and Mapping (SLAM), using the time difference of arrival measurements between two microphones (TDOA-M), and the robot (which serves as a moving sound source during calibration) odometry information. In this paper, we introduce a new form of measurement for microphone array calibration, i.e. the time difference of arrival between adjacent sound events (TDOA-S) with respect to the microphone channels. We propose to use TDOA-S and TDOA-M, called hybrid TDOA, together with odometry measurements for bath SLAM-based calibration of asynchronous microphone arrays. Extensive simulation and real-world experiments show that our method is more independent of microphone number, less sensitive to initial values (when using off-the-shelf algorithms such as Gauss-Newton iterations), and has better calibration accuracy and robustness under various TDOA noises. Simulation results also demonstrate that our method has a lower Cram\'er-Rao lower bound (CRLB) for microphone parameters. To benefit the community, we open-source our code and data at https://github.com/AISLAB-sustech/Hybrid-TDOA-Calib.
Trust Model for Reliable Grouping-Based Communications in Vehicular Ad-Hoc Networks
Authors: MUHAMMAD HALEEM JUNEJO, AB AL-HADI BIN AB RAHMAN, RIAZ AHMED SHAIKH, KAMALUDIN MOHAMAD YUSOF, SHAHIDATUL SADIAH
Date: 2023-11-02 00:00:00
Proceeding: IEEE Access 2023 11

This paper presents a high-reliability Grouping-Based communications trust model in
Vehicular Ad-hoc Networks (VANET). The proposed solution consists of two distinct components: First, the
Dynamic Group Head Selection (DGHS) scheme to improve the Group Head’s (GH) stability by considering
the reliability of the communication link with the Road Side Unit (RSU). Second, a hybrid Dynamic Trust
Model (DTM) scheme improves trustworthiness by considering the node’s dynamic conditions and records.
Average GH Lifetime and Average Query Success Rate were used as metrics to evaluate the performance
of the grouping algorithms to those of Cluster-Based Location Service (CBLS) and Trust-based Security
for Message Exchange (TSME). The proposed DTM was compared to TSME and MiTM Attack Resistant
Trust Model (MARINE). With a maximum density of 900 vehicles, DTM improves the average GH lifetime
by 43% compared to CBLS and 19% compared to TSME. Similarly, DTM increases the query success rate
by 10% compared to the CBLS and 23% compared to the TSME, even at the slowest speed (40 km/h).
In terms of node density precision, DTM decreases by around 5% for MARINE and 10% for the TSME
approach. In conclusion, it has been shown that the proposed schemes are reliable for various position-based
VANET applications that need precise positioning and trusted messaging.
SonoTraceLab - A Raytracing-Based Acoustic Modelling System for Simulating Echolocation Behavior of Bats
Authors: Wouter Jansen, Jan Steckel
Date: 2024-03-11 00:00:00
Tasks: Acoustic Modelling

Echolocation is the prime sensing modality for many species of bats, who show the intricate ability to perform a plethora of tasks in complex and unstructured environments. Understanding this exceptional feat of sensorimotor interaction is a key aspect into building more robust and performant man-made sonar sensors. In order to better understand the underlying perception mechanisms it is important to get a good insight into the nature of the reflected signals that the bat perceives. While ensonification experiments are in important way to better understand the nature of these signals, they are as time-consuming to perform as they are informative. In this paper we present SonoTraceLab, an open-source software package for simulating both technical as well as biological sonar systems in complex scenes. Using simulation approaches can drastically increase insights into the nature of biological echolocation systems, while reducing the time- and material complexity of performing them.
A Microwave Imaging System for Soil Moisture Estimation in Subsurface Drip Irrigation
Authors: Mohammad Ramezaninia, Mohammad Zoofaghari
Date: 2024-03-08 00:00:00
Tasks: Soil moisture estimation

The microwave imaging system(MIS) stands out among prominent imaging tools for capturing images of concealed obstacles. Leveraging its capability to penetrate through heterogeneous environments MIS has been widely used for subsurface imaging. Monitoring subsurface drip irrigation(SDI) as an efficient procedure in agricultural irrigation is essential to maintain the required moisture percentage for plant growth which is a novel MIS application. In this research, we implement a laboratory-scale MIS for SDI reflecting real-world conditions to evaluate leakage localization and quantification in a heterogeneous area. We extract a model to quantify the moisture content by exploiting an imaging approach that could be used in a scheduled SDI. We employ the subspace information of images formed by back projection and Born approximation algorithms for model parametrization and estimate the model parameters using a statistical curve fitting technique. We then compare the performance of these imaging techniques in the presence of environmental clutter such as plant roots and pebbles. The proposed approach can well contribute to efficient mechanistic subsurface irrigation for which the local moisture around the root is obtained noninvasively and remotely with less than 20% estimation error.
Proximity-Informed Calibration for Deep Neural Networks
Authors: Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, Bryan Hooi
Date: 2023-06-07 00:00:00
Proceeding: NeurIPS 2023 11

Confidence calibration is central to providing accurate and interpretable uncertainty estimates, especially under safety-critical scenarios. However, we find that existing calibration algorithms often overlook the issue of *proximity bias*, a phenomenon where models tend to be more overconfident in low proximity data (i.e., data lying in the sparse region of the data distribution) compared to high proximity samples, and thus suffer from inconsistent miscalibration across different proximity samples. We examine the problem over 504 pretrained ImageNet models and observe that: 1) Proximity bias exists across a wide variety of model architectures and sizes; 2) Transformer-based models are relatively more susceptible to proximity bias than CNN-based models; 3) Proximity bias persists even after performing popular calibration algorithms like temperature scaling; 4) Models tend to overfit more heavily on low proximity samples than on high proximity samples. Motivated by the empirical findings, we propose ProCal, a plug-and-play algorithm with a theoretical guarantee to adjust sample confidence based on proximity. To further quantify the effectiveness of calibration algorithms in mitigating proximity bias, we introduce proximity-informed expected calibration error (PIECE) with theoretical analysis. We show that ProCal is effective in addressing proximity bias and improving calibration on balanced, long-tail, and distribution-shift settings under four metrics over various model architectures. We believe our findings on proximity bias will guide the development of *fairer and better-calibrated* models, contributing to the broader pursuit of trustworthy AI. Our code is available at: https://github.com/MiaoXiong2320/ProximityBias-Calibration.
A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models
Authors: Koen C. Mertens
Date: 2007-02-22 00:00:00
Proceeding: International Journal of Remote Sensing 2007 2
Tasks: Classification

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
Authors: Chongyu Fan, Jiancheng Liu, Alfred Hero, Sijia Liu
Date: 2024-03-12 00:00:00
Tasks: Machine Unlearning

The trustworthy machine learning (ML) community is increasingly recognizing the crucial need for models capable of selectively 'unlearning' data points after training. This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on random data forgetting, ignoring the vital inquiry into which subset should be chosen to truly gauge the authenticity of unlearning performance. To tackle this issue, we introduce a new evaluative angle for MU from an adversarial viewpoint. We propose identifying the data subset that presents the most significant challenge for influence erasure, i.e., pinpointing the worst-case forget set. Utilizing a bi-level optimization principle, we amplify unlearning challenges at the upper optimization level to emulate worst-case scenarios, while simultaneously engaging in standard training and unlearning at the lower level, achieving a balance between data influence erasure and model utility. Our proposal offers a worst-case evaluation of MU's resilience and effectiveness. Through extensive experiments across different datasets (including CIFAR-10, 100, CelebA, Tiny ImageNet, and ImageNet) and models (including both image classifiers and generative models), we expose critical pros and cons in existing (approximate) unlearning strategies. Our results illuminate the complex challenges of MU in practice, guiding the future development of more accurate and robust unlearning algorithms. The code is available at https://github.com/OPTML-Group/Unlearn-WorstCase.
FSC: Few-point Shape Completion
Authors: Xianzu Wu, Xianfeng Wu, Tianyu Luan, Yajing Bai, Zhongyuan Lai, Junsong Yuan
Date: 2024-03-12 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decoder, Object

While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e.g. tens of points, are observed. Surprisingly, via entropy analysis, we find that even a few points, e.g. 64 points, could retain substantial information to help recover the 3D shape of the object. To address the challenge of shape completion with very sparse point clouds, we then propose Few-point Shape Completion (FSC) model, which contains a novel dual-branch feature extractor for handling extremely sparse inputs, coupled with an extensive branch for maximal point utilization with a saliency branch for dynamic importance assignment. This model is further bolstered by a two-stage revision network that refines both the extracted features and the decoder output, enhancing the detail and authenticity of the completed point cloud. Our experiments demonstrate the feasibility of recovering 3D shapes from a few points. The proposed Few-point Shape Completion (FSC) model outperforms previous methods on both few-point inputs and many-point inputs, and shows good generalizability to different object categories.
Energy versus Output Quality of Non-volatile Writes in Intermittent Computing
Authors: Rei Barjami, Antonio Miele, Luca Mottola
Date: 2024-03-12 00:00:00

We explore how to improve the energy performance of battery-less Internet of Things (IoT) devices at the cost of a reduction in the quality of the output. Battery-less IoT devices are extremely resource-constrained energy-harvesting devices. Due to erratic energy patterns from the ambient, their executions become intermittent; periods of active computation are interleaved by periods of recharging small energy buffers. To cross periods of energy unavailability, a device persists application and system state onto Non-Volatile Memory (NVM) in anticipation of energy failures. We purposely control the energy invested in these operations, representing a major energy overhead, when using Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) as NVM. As a result, we abate the corresponding overhead, yet introduce write errors. Based on 1.9+ trillion experimental data points, we illustrate whether this is a gamble worth taking, when, and where. We measure the energy consumption and quality of output obtained from the execution of nine diverse benchmarks on top of seven different platforms. Our results allow us to draw three key observations: i) the trade-off between energy saving and reduction of output quality is program-specific; ii) the same trade-off is a function of a platform's specific compute efficiency and power figures; and iii) data encoding and input size impact a program's resilience to errors. As a paradigmatic example, we reveal cases where we achieve up to 50% reduction in energy consumption with negligible effects on output quality, as opposed to settings where a minimal energy gain causes drastic drops in output quality.
SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference
Authors: Yanming Liu, Xinyue Peng, Shi Bo, Ningjing Sang, Yafeng Yan, Xiaolan Ke, Zhiting Zheng, Shaobo Liu, Songhang Deng, Jiannan Cao, Le Dai, Xingzu Liu, Ruilin Nong, Weihao Liu
Date: 2024-03-11 00:00:00
Tasks: Question Answering, Single Particle Analysis

Large language models(LLMs) have shown its outperforming ability on various tasks and question answering. However, LLMs require substantial memory storage on low-resource devices. More critically, the computational speed on these devices is also severely limited. In this paper, we propose SPA(Side Plugin Adaption), a lightweight architecture for fast on-devices inference on the constraints of strict on-devices computation and memory constraints. Compared with other on-devices seq2seq generation, SPA could make a fast and stable inference on low-resource constraints, allowing it to obtain cost effiency. Our method establish an interaction between a pretrained LLMs on-cloud and additive parameters on-devices, which could provide the knowledge on both pretrained LLMs and featured personal feature. Further more, SPA provides a framework to keep feature-base parameters on low computational devices while leave the parameters containing general information on the high computational devices.