pwc-archive/papers-with-abstracts huggingface dataset:
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation
Authors: Wenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen
Date: 2024-01-01 00:00:00
Tasks: Knowledge Distillation, Recommendation Systems, Sequential Recommendation

Pre-trained recommendation models (PRMs) have received increasing interest recently. However, their intrinsically heterogeneous model structure, huge model size and computation cost hinder their adoptions in practical recommender systems. Hence, it is highly essential to explore how to use different pre-trained recommendation models efficiently in real-world systems. In this paper, we propose a novel curriculum-scheduled knowledge distillation from multiple pre-trained teachers for multi-domain sequential recommendation, called CKD-MDSR, which takes full advantages of different PRMs as multiple teacher models to boost a small student recommendation model, integrating the knowledge across multiple domains from PRMs. Specifically, CKD-MDSR first adopts curriculum-scheduled user behavior sequence sampling and distills informative knowledge jointly from the representative PRMs such as UniSRec and Recformer. Then, the knowledge from the above PRMs are selectively integrated into the student model in consideration of their confidence and consistency. Finally, we verify the proposed method on multi-domain sequential recommendation and further demonstrate its universality with multiple types of student models, including feature interaction and graph based recommendation models. Extensive experiments on five real-world datasets demonstrate the effectiveness and efficiency of CKD-MDSR, which can be viewed as an efficient shortcut using PRMs in real-world systems.
Recent Advances in Text Analysis
Authors: Zheng Tracy Ke, Pengsheng Ji, Jiashun Jin, Wanshan Li
Date: 2024-01-01 00:00:00

Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic modeling, and discuss how to use it to analyze MADStat - a dataset on statistical publications that we collected and cleaned. The application of Topic-SCORE and other methods on MADStat leads to interesting findings. For example, $11$ representative topics in statistics are identified. For each journal, the evolution of topic weights over time can be visualized, and these results are used to analyze the trends in statistical research. In particular, we propose a new statistical model for ranking the citation impacts of $11$ topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another. The results on MADStat provide a data-driven picture of the statistical research in $1975$--$2015$, from a text analysis perspective.
V2X communication coverage analysis for connected vehicles in intelligent transportation networks: A case study for the city of Xanthi, Greece
Authors: Evangelos Bazinas, Andreas Gregoriades, Marios Raspopoulos, Michael Georgiades
Date: 2023-12-31 00:00:00
Tasks: Management

Intelligent transportation systems (ITS) have been developed to improve traffic flow, efficiency, and safety in transportation. Technological advancements in communication such as the Vehicle-to-Everything (V2X), Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) enable the real-time exchange of information between vehicles and other entities on the road network, and thus play a significant role in their safety and efficiency. This paper presents a simulation study that models V2V and V2I communication to identify the most suitable range of data transmission between vehicles and infrastructure. The provincial city of Xanthi, Greece is used as a cases study, and the goal is to evaluate whether the proposed placement of Road Side Unit (RSU) provided adequate communication coverage on the city's road network. An analysis through different scenarios identified improvements in traffic management, driving behavior and environmental conditions under different RSU coverage. The results highlight that the communication range of 400 meters is the most adequate option for optimum traffic management in the city of Xanthi.
EXPLORE -- Explainable Song Recommendation
Authors: Abhinav Arun, Mehul Soni, Palash Choudhary, Saksham Arora
Date: 2023-12-30 00:00:00
Tasks: Collaborative Filtering, Music Recommendation

This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive user interface. Our methodology integrates Spotify data with user preference analytics to tailor music suggestions. Evaluation through RMSE and user studies underscores the efficacy and user satisfaction with our system. The paper concludes with potential directions for future enhancements in group recommendations and dynamic feedback integration.
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation
Authors: Can Chen, Hao liu, Zeming Liu, Xue Liu, Dejing Dou
Date: 2023-12-30 00:00:00
Tasks: Conversational Recommendation, Recommendation Systems, Representation Learning

Proactively and naturally guiding the dialog from the non-recommendation context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is crucial for the Conversational Recommender System (CRS). Prior studies mainly focus on planning the next dialog goal~(e.g., chat on a movie star) conditioned on the previous dialog. However, we find the dialog goals can be simultaneously observed at different levels, which can be utilized to improve CRS. In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation. Specifically, we exploit multi-level goal sequences from both the representation space and the optimization space. In the representation space, we propose the hierarchical representation learning where a cross attention module derives mutually enhanced multi-level goal representations. In the optimization space, we devise the hierarchical weight learning to reweight lower-level goal sequences, and introduce bi-level optimization for stable update. Additionally, we propose a soft labeling strategy to guide optimization gradually. Experiments on two real-world datasets verify the effectiveness of our approach. Code and data are available here.
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Authors: Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, Joost Van de Weijer
Date: 2023-09-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Prompt Learning, Text-based Image Editing

Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are susceptible to unintended modifications of regions outside the targeted area, such as on the background or on distractor objects which have some semantic or visual relationship with the targeted object. According to our experimental findings, inaccurate cross-attention maps are at the root of this problem. Based on this observation, we propose Dynamic Prompt Learning (DPL) to force cross-attention maps to focus on correct noun words in the text prompt. By updating the dynamic tokens for nouns in the textual input with the proposed leakage repairment losses, we achieve fine-grained image editing over particular objects while preventing undesired changes to other image regions. Our method DPL, based on the publicly available Stable Diffusion, is extensively evaluated on a wide range of images, and consistently obtains superior results both quantitatively (CLIP score, Structure-Dist) and qualitatively (on user-evaluation). We show improved prompt editing results for Word-Swap, Prompt Refinement, and Attention Re-weighting, especially for complex multi-object scenes.
Local Inter-Session Variability Modelling for Object Classification
Authors: Kaneswaran Anantharajah
Date: 2014-06-23 00:00:00
Proceeding: IEEE Winter Conference on Applications of Computer Vision 2014 6
Tasks: Classification, Face Verification, Object

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification.
In this paper we propose a local region based inter-session variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset
LineEX: Data Extraction from Scientific Line Charts
Authors: Shivasankaran V P, Muhammad Yusuf Hassan, Mayank Singh
Date: 2023-02-06 00:00:00
Proceeding: Winter Conference on Applications of Computer Vision 2023 2

In this paper, we introduce LINEEX that extracts data from scientific line charts. We adapt existing vision transformers and pose detection methods and showcase significant performance gains over existing SOTA baselines. We also propose a new loss function and present its effectiveness against existing loss functions. In addition, we synthetically created the largest line chart dataset comprising 430K images.
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
Authors: Jiang You, Arben Cela, René Natowicz, Jacob Ouanounou, Patrick Siarry
Date: 2024-01-03 00:00:00
Tasks: Computational Efficiency, Decoder, Image Segmentation, Medical Image Segmentation, Multivariate Time Series Forecasting, Semantic Segmentation, Time Series, Time Series Forecasting

Time series forecasting task predicts future trends based on historical information. Transformer-based U-Net architectures, despite their success in medical image segmentation, have limitations in both expressiveness and computation efficiency in time series forecasting as evidenced in YFormer. To tackle these challenges, we introduce Kernel-U-Net, a flexible and kernel-customizable U-shape neural network architecture. The kernel-U-Net encoder compresses the input series into latent vectors, and its symmetric decoder subsequently expands these vectors into output series. Specifically, Kernel-U-Net separates the procedure of partitioning input time series into patches from kernel manipulation, thereby providing the convenience of customized executing kernels. Our method offers two primary advantages: 1) Flexibility in kernel customization to adapt to specific datasets; and 2) Enhanced computational efficiency, with the complexity of the Transformer layer reduced to linear. Experiments on seven real-world datasets, demonstrate that Kernel-U-Net's performance either exceeds or meets that of the existing state-of-the-art model in the majority of cases in channel-independent settings. The source code for Kernel-U-Net will be made publicly available for further research and application.
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
Authors: Qingyuan Yang, Guanzhou Chen, Xiaoliang Tan, Tong Wang, Jiaqi Wang, Xiaodong Zhang
Date: 2024-01-03 00:00:00
Tasks: 3D Reconstruction, Disparity Estimation, Segmentation, Semantic Segmentation, Stereo Matching

Stereo matching and semantic segmentation are significant tasks in binocular satellite 3D reconstruction. However, previous studies primarily view these as independent parallel tasks, lacking an integrated multitask learning framework. This work introduces a solution, the Single-branch Semantic Stereo Network (S3Net), which innovatively combines semantic segmentation and stereo matching using Self-Fuse and Mutual-Fuse modules. Unlike preceding methods that utilize semantic or disparity information independently, our method dentifies and leverages the intrinsic link between these two tasks, leading to a more accurate understanding of semantic information and disparity estimation. Comparative testing on the US3D dataset proves the effectiveness of our S3Net. Our model improves the mIoU in semantic segmentation from 61.38 to 67.39, and reduces the D1-Error and average endpoint error (EPE) in disparity estimation from 10.051 to 9.579 and 1.439 to 1.403 respectively, surpassing existing competitive methods. Our codes are available at:https://github.com/CVEO/S3Net.
ColorizeDiffusion: Adjustable Sketch Colorization with Reference Image and Text
Authors: Dingkun Yan, Liang Yuan, Erwin Wu, Yuma Nishioka, Issei Fujishiro, Suguru Saito
Date: 2024-01-02 00:00:00
Tasks: Colorization, Sketch Colorization

Diffusion models have recently demonstrated their effectiveness in generating extremely high-quality images and are now utilized in a wide range of applications, including automatic sketch colorization. Although many methods have been developed for guided sketch colorization, there has been limited exploration of the potential conflicts between image prompts and sketch inputs, which can lead to severe deterioration in the results. Therefore, this paper exhaustively investigates reference-based sketch colorization models that aim to colorize sketch images using reference color images. We specifically investigate two critical aspects of reference-based diffusion models: the "distribution problem", which is a major shortcoming compared to text-based counterparts, and the capability in zero-shot sequential text-based manipulation. We introduce two variations of an image-guided latent diffusion model utilizing different image tokens from the pre-trained CLIP image encoder and propose corresponding manipulation methods to adjust their results sequentially using weighted text inputs. We conduct comprehensive evaluations of our models through qualitative and quantitative experiments as well as a user study.
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
Authors: Feng Zhang, Ming Tian, Zhiqiang Li, Bin Xu, Qingbo Lu, Changxin Gao, Nong Sang
Date: 2023-10-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Computational Efficiency, Image Enhancement, Low-Light Image Enhancement, Tone Mapping

Tone mapping aims to convert high dynamic range (HDR) images to low dynamic range (LDR) representations, a critical task in the camera imaging pipeline. In recent years, 3-Dimensional LookUp Table (3D LUT) based methods have gained attention due to their ability to strike a favorable balance between enhancement performance and computational efficiency. However, these methods often fail to deliver satisfactory results in local areas since the look-up table is a global operator for tone mapping, which works based on pixel values and fails to incorporate crucial local information. To this end, this paper aims to address this issue by exploring a novel strategy that integrates global and local operators by utilizing closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we employ image-adaptive 3D LUTs to manipulate the tone in the low-frequency image by leveraging the specific characteristics of the frequency information. Furthermore, we utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner. Local Laplacian filters are widely used to preserve edge details in photographs, but their conventional usage involves manual tuning and fixed implementation within camera imaging pipelines or photo editing tools. We propose to learn parameter value maps progressively for local Laplacian filters from annotated data using a lightweight network. Our model achieves simultaneous global tone manipulation and local edge detail preservation in an end-to-end manner. Extensive experimental results on two benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.
Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease
Authors: Muhammad Arqam, Majid Hussain, Hina Zafar, Amna Iqbal, Maria Liaqat
Date: 2023-12-11 00:00:00
Proceeding: 2023 2023 12
Tasks: Word Embeddings

Even with better tools for diagnosis and prevention, heart disease continues to be a major killer. Diagnosis and prevention of heart disease require detection and awareness of risk factors. Disease progression modeling and clinical decisionmaking can both benefit from the automatic identification of these risk markers from clinical records. Many studies have tried, without success, to uncover the characteristics that put people at risk for cardiovascular disease. Many hybrid systems have been suggested that attempt to integrate knowledgedriven and data-driven approaches, however due to the usage of Machine Learning, rules, and dictionaries, methods, they frequently require substantial human input. Natural language processing and deep learning methods can be used to extract useful information from clinical tales. By using state-of-the-art methods involving stacked word embeddings to the 2014 i2b2 challenge, this article hopes to contribute to the state of the art in this area. The suggested model makes use of a stacking method that combines different embeddings (CHARACTERBERT Embedding) to better understand the data. Our model shows substantial improvement after using this strategy to the i2b2 heart disease risk factors challenge dataset. An F1 score of 92.65% is an excellent result. When compared to competing models and systems created for the 2014 i2b2 challenge, our proposed model fares exceptionally well.
Step length measurement in the wild using FMCW radar
Authors: Parthipan Siva, Alexander Wong, Patricia Hewston, George Ioannidis, Dr. Jonathan Adachi, Dr. Alexander Rabinovich, Andrea Lee, Alexandra Papaioannou
Date: 2024-01-03 00:00:00
Tasks: Privacy Preserving

With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place predicting risk factors such as falls, and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length, in addition to gait speed, is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof of concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using radar point cloud, followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment, involving 35 frail older adults, to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold standard Zeno Walkway Gait Analysis System, revealing a 4.5cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k)=0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong agreement (ICC(3,k)=0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
Authors: Uri Shaham, Jonathan Herzig, Roee Aharoni, Idan Szpektor, Reut Tsarfaty, Matan Eyal
Date: 2024-01-03 00:00:00
Tasks: Cross-Lingual Transfer, Instruction Following

As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. In this work, we investigate how multilinguality during instruction tuning of a multilingual LLM affects instruction-following across languages from the pre-training corpus. We first show that many languages transfer some instruction-following capabilities to other languages from even monolingual tuning. Furthermore, we find that only 40 multilingual examples integrated in an English tuning set substantially improve multilingual instruction-following, both in seen and unseen languages during tuning. In general, we observe that models tuned on multilingual mixtures exhibit comparable or superior performance in multiple languages compared to monolingually tuned models, despite training on 10x fewer examples in those languages. Finally, we find that diversifying the instruction tuning set with even just 2-4 languages significantly improves cross-lingual generalization. Our results suggest that building massively multilingual instruction-tuned models can be done with only a very small set of multilingual instruction-responses.
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
Authors: Daniel Geißler, Bo Zhou, Mengxi Liu, Sungho Suh, Paul Lukowicz
Date: 2024-01-03 00:00:00
Tasks: Transfer Learning

This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective. While increasing data availability and innovation in high-performance hardware fuels the training of sophisticated models, it also fosters the fading perception of energy consumption and carbon emission. Therefore, the goal of this work is to raise awareness about the energy impact of general training parameters and processes, from learning rate over batch size to knowledge transfer. Multiple setups with different hyperparameter configurations are evaluated on three different hardware systems. Among many results, we have found out that even with the same model and hardware to reach the same accuracy, improperly set training hyperparameters consume up to 5 times the energy of the optimal setup. We also extensively examined the energy-saving benefits of learning paradigms including recycling knowledge through pretraining and sharing knowledge through multitask training.