pwc-archive/papers-with-abstracts huggingface dataset:
UniTS: A Unified Multi-Task Time Series Model
Authors: ShangHua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, Marinka Zitnik
Date: 2024-02-29 00:00:00
Tasks: Anomaly Detection, Imputation, model, Time Series, Time Series Forecasting

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, with most models narrowly focused on specific areas, such as time series forecasting. Unifying predictive and generative time series tasks within a single model remains challenging. We introduce UniTS, a unified multi-task time series model that utilizes task tokenization to integrate predictive and generative tasks into a single framework. UniTS employs a modified transformer block to capture universal time series representations, enabling transferability from a heterogeneous, multi-domain pre-training dataset-characterized by diverse dynamic patterns, sampling rates, and temporal scales-to a wide range of downstream datasets with varied task specifications and data domains. Tested on 38 datasets across human activity sensors, healthcare, engineering, and finance, UniTS achieves superior performance compared to 12 forecasting models, 20 classification models, 18 anomaly detection models, and 16 imputation models, including adapted text-based LLMs. UniTS also demonstrates strong few-shot and prompt capabilities when applied to new domains and tasks. In single-task settings, UniTS outperforms competitive task-specialized time series models. Code and datasets are available at https://github.com/mims-harvard/UniTS.
Resonance RoPE: Improving Context Length Generalization of Large Language Models
Authors: Suyuchen Wang, Ivan Kobyzev, Peng Lu, Mehdi Rezagholizadeh, Bang Liu
Date: 2024-02-29 00:00:00
Tasks: Language Modeling, Language Modelling, Position

This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions, significantly improving the model performance without additional online computational costs. Furthermore, we present PosGen, a new synthetic benchmark specifically designed for fine-grained behavior analysis in TSTL scenarios, aiming to isolate the constantly increasing difficulty of token generation on long contexts from the challenges of recognizing new token positions. Our experiments on synthetic tasks show that after applying Resonance RoPE, Transformers recognize OOD position better and more robustly. Our extensive LLM experiments also show superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and a variety of downstream long-text applications.
Region-Adaptive Transform with Segmentation Prior for Image Compression
Authors: Yuxi Liu, Wenhan Yang, Huihui Bai, Yunchao Wei, Yao Zhao
Date: 2024-03-01 00:00:00
Tasks: Image Compression, Segmentation

Learned Image Compression (LIC) has shown remarkable progress in recent years. Existing works commonly employ CNN-based or self-attention-based modules as transform methods for compression. However, there is no prior research on neural transform that focuses on specific regions. In response, we introduce the class-agnostic segmentation masks (i.e. semantic masks without category labels) for extracting region-adaptive contextual information. Our proposed module, Region-Adaptive Transform, applies adaptive convolutions on different regions guided by the masks. Additionally, we introduce a plug-and-play module named Scale Affine Layer to incorporate rich contexts from various regions. While there have been prior image compression efforts that involve segmentation masks as additional intermediate inputs, our approach differs significantly from them. Our advantages lie in that, to avoid extra bitrate overhead, we treat these masks as privilege information, which is accessible during the model training stage but not required during the inference phase. To the best of our knowledge, we are the first to employ class-agnostic masks as privilege information and achieve superior performance in pixel-fidelity metrics, such as Peak Signal to Noise Ratio (PSNR). The experimental results demonstrate our improvement compared to previously well-performing methods, with about 8.2% bitrate saving compared to VTM-17.0. The source code is available at https://github.com/GityuxiLiu/SegPIC-for-Image-Compression.
Did We Get It Right? Predicting Query Performance in E-commerce Search
Authors: Rohan Kumar, Mohit Kumar, Neil Shah, Christos Faloutsos
Date: 2018-08-01 00:00:00

In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. This is a critical question in evaluating any e-commerce search engine. While this question is traditionally answered using simple metrics like query click-through rate (CTR), we observe that in e-commerce search, such metrics can be misleading. Upon inspection, we find cases where CTR is high but the results are poor and vice versa. Similar cases exist for other metrics like time to click which are often also used for evaluating search engines. We aim to learn the quality of the results served by the search engine based on users' interactions with the results. Although this problem has been studied in the web search context, this is the first study for e-commerce search, to the best of our knowledge. Despite certain commonalities with evaluating web search engines, there are several major differences such as underlying reasons for search failure, and availability of rich user interaction data with products (e.g. adding a product to the cart). We study large-scale user interaction logs from Flipkart's search engine, analyze behavioral patterns and build models to classify queries based on user behavior signals. We demonstrate the feasibility and efficacy of such models in accurately predicting query performance. Our classifier is able to achieve an average AUC of 0.75 on a held-out test set.
ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
Authors: Sijun Dong, Libo Wang, Bo Du, Xiaoliang Meng
Date: 2024-01-04 00:00:00
Proceeding: journal 2024 1
Tasks: Change Detection, Decoder, Language Modelling, Representation Learning

Remote sensing change detection (RSCD), which aims to identify surface changes from bitemporal images, is significant for many applications, such as environmental protection and disaster monitoring. In the last decade, driven by the wave of artificial intelligence, many change detection methods based on deep learning emerged and have achieved essential breakthroughs. However, these methods pay more attention to visual representation learning while ignoring the potential of multimodal data. Recently, the foundation vision-language model, i.e. CLIP, has provided a new paradigm for multimodal AI, demonstrating impressive performance on downstream tasks. Following this trend, in this study, we introduce ChangeCLIP, a novel framework that leverages robust semantic information from image-text pairs, specifically tailored for Remote Sensing Change Detection (RSCD). Specifically, we reconstruct the original CLIP to extract bitemporal features and propose a novel differential features compensation module to capture the detailed semantic changes between them. Besides, we proposed a vision-language-driven decoder by combining the results of image-text encoding with the visual features of the decoding stage, thereby enhancing the image semantics. The proposed ChangeCLIP achieved state-of-the-art IoU on 5 well-known change detection datasets, LEVIR-CD (85.20%), LEVIR-CD+ (75.63%), WHUCD (90.15%), CDD (95.87%) and SYSU-CD (71.41%). The code and the pretrained models of ChangeCLIP will be publicly available on https://github.com/dyzy41/ChangeCLIP.
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System
Authors: Zihan Wu, HONGZHANG, PENGHAI WANG, ANDZHIBOSUN
Date: 2022-06-13 00:00:00
Proceeding: journal 2022 6
Tasks: Decoder, Intrusion Detection

Due to the rapid growth in network traf c and increasing security threats, Intrusion Detec
tion Systems (IDS) have become increasingly critical in the eld of cyber security for providing secure
 communications against cyber adversaries. However, there exist many challenges for designing a robust,
 ef cient and accurate IDS, especially when dealing with high-dimensional anomaly data with unforeseen
 and unpredictable attacks. In this paper, we propose a Robust Transformer-based Intrusion Detection
 System(RTIDS)reconstructingfeaturerepresentationstomakeatrade-offbetweendimensionalityreduction
 and feature retention in imbalanced datasets. The proposed method utilizes positional embedding technique
 to associate sequential information between features, then a variant stacked encoder-decoder neural network
 is used to learn low-dimensional feature representations from high-dimensional raw data. Furthermore,
 we apply self-attention mechanism to facilitate network traf c type classi cations. Extensive experiments
 reveal the effectiveness of the proposed RTIDS on two publicly available real traf c intrusion detection
 datasets named CICIDS2017 and CIC-DDoS2019 with F1-Score of 99.17% and 98.48% respectively.
 A comparative study with classical machine learning algorithm support vector machine (SVM) and deep
 learning algorithms that include recurrent neural network (RNN), fuzzy neural network (FNN), and long
 short-term memory network (LSTM) is conducted to demonstrate the validity of the proposed method.
Low rank label subspace transformation for multi-label learning with missing labels
Authors: Sanjay Kumar, Reshma Rastogi
Date: 2022-03-05 00:00:00
Proceeding: Information Sciences 2022 3
Tasks: Missing Labels, Missing Values, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Multi-label datasets often contain label information with missing values and recovering them is a non-trivial challenge. Several methods augment the observed label matrix by constructing auxiliary labels and learning high order label correlations. Some other techniques exploit the low rank of the label matrix to capture a mix of label correlations. Both these approaches rely on label correlations, however in different ways. In this paper, we propose a unified framework that captures the label correlations utilizing both auxiliary label matrix and the low rank constraints on estimated labels. Our model also enforces maximal separation among different label subspaces for better label differentiation. The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label matrix which facilitates the missing label information recovery. Low rank on predictions ensures that local label structures are captured and the maximal inter-label subspace separation helps identify discriminatory label correlations. The proposed method builds a multi-label classification model by solving a multivariate difference of convex objective function using surrogate optimization technique and alternating minimization. Empirical results on several benchmark datasets validate the effectiveness of the proposed method against state-of-the-art multi-label learning approaches.
Multi-label learning with missing labels using sparse global structure for label-specific features
Authors: Sanjay Kumar, Nadira Ahmadi, Reshma Rastogi
Date: 2023-01-24 00:00:00
Proceeding: Applied Intelligence Journal 2023 1
Tasks: Missing Labels, Multi-Label Learning

Multi-label learning associates a given data instance with one or several class labels. A frequent problem with real life multi-label datasets is the lack of complete label information. Incomplete labels increase model complexity as the label correlation information is not reliable, resulting in a suboptimal multi-label classifier. Further, high dimensionality of multi-label datasets often introduces spurious feature-label dependencies. Thus, discovering label-specific features is imperative for efficient handling of high-dimensional data for multi-label learning with missing labels. To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data. Instances are expressed as linear combination of label-specific features and the inter-relation guides the construction of model coefficients. The model also constructs supplementary label correlations to assist missing label recovery as part of the optimization problem. Empirical results on benchmark multi-label datasets highlight the effectiveness of the proposed method.
Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels
Authors: Reshma Rastogi, Sanjay Kumar
Date: 2022-07-02 00:00:00
Proceeding: Neural Processing Letters 2022 7
Tasks: Missing Labels, Multi-Label Classification, Multi-Label Learning

Class labels in multi-label datasets are only associated with a very small fraction of the data instances leading to a class imbalance problem. There exist multi-label learning algorithms that handle the datasets’ class imbalance issue by assigning favorable weights for positive labels to compensate for their scarce occurrence. The weight assignment assumes full label information and doesn’t provision for the uncertainty introduced by unobserved or missing labels. Therefore, to deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved. Utilizing the class imbalance sensitive weights and auxiliary label correlations, we introduce a weighted squared loss function with discriminatory label weights which guides the missing label completion and learns the multi-label classifier efficiently. Extensive experiments on several benchmark datasets validate the effectiveness of proposed classifier against state-of-the-art multi-label learning approaches for missing labels underlining the significance of handling label imbalance for multi-label learning for missing labels.
Auxiliary Label Embedding for Multi-label Learning with Missing Labels
Authors: Sanjay Kumar, Reshma Rastogi
Date: 2023-05-06 00:00:00
Proceeding: International Conference on Computer Vision and Machine Intelligence (CVMI-2022) 2023 5
Tasks: Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Label correlation has been exploited for multi-label learning in different ways. Existing approaches presume that label correlation information is available as a prior, but for multi-label datasets having incomplete labels, the assumption is violated. In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients. The approach recovers missing labels and simultaneously guides the construction of model coefficients from the learnt label correlations. Empirical results on multi-label datasets from diverse domains such as image & music substantiate the correlation embedding approach for missing label scenario. The proposed approach performs favorably over four popular multi-label learning techniques using five multi-label evaluation metrics.
Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
Authors: Hugo Schnoering, Pierre Porthaux, Michalis Vazirgiannis
Date: 2024-03-01 00:00:00
Tasks: Clustering

Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alongside the introduction of four novel ones. Our contribution includes the introduction of the \textit{clustering ratio}, a metric designed to quantify the reduction in the number of entities achieved by a given heuristic. The assessment of this reduction ratio plays an important role in justifying the selection of a specific heuristic for analytical purposes. Given the dynamic nature of the Bitcoin system, characterized by a continuous increase in the number of entities on the blockchain, and the evolving behaviors of these entities, we extend our study to explore the temporal evolution of the clustering ratio for each heuristic. This temporal analysis enhances our understanding of the effectiveness of these heuristics over time.
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Authors: Amirul Islam Saimon, Emmanuel Yangue, Xiaowei Yue, Zhenyu James Kong, Chenang Liu
Date: 2024-03-01 00:00:00

This paper presents the first comprehensive literature review of deep learning (DL) applications in additive manufacturing (AM). It addresses the need for a thorough analysis in this rapidly growing yet scattered field, aiming to bring together existing knowledge and encourage further development. Our research questions cover three major areas of AM: (i) design for AM, (ii) AM modeling, and (iii) monitoring and control in AM. We use a step-by-step approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select papers from Scopus and Web of Science databases, aligning with our research questions. We include only those papers that implement DL across seven major AM categories - binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet lamination, and vat photopolymerization. Our analysis reveals a trend towards using deep generative models, such as generative adversarial networks, for generative design in AM. It also highlights an increasing effort to incorporate process physics into DL models to improve AM process modeling and reduce data requirements. Additionally, there is growing interest in using 3D point cloud data for AM process monitoring, alongside traditional 1D and 2D formats. Finally, this paper summarizes the current challenges and recommends some of the promising opportunities in this domain for further investigation with a special focus on (i) generalizing DL models for a wide range of geometry types, (ii) managing uncertainties both in AM data and DL models, (iii) overcoming limited, imbalanced, and noisy AM data issues by incorporating deep generative models, and (iv) unveiling the potential of interpretable DL for AM.
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything
Authors: Safouane El Ghazouali, Youssef Mhirit, Ali Oukhrid, Umberto Michelucci, Hichem Nouira
Date: 2024-02-29 00:00:00
Tasks: 3D Object Reconstruction, Instance Segmentation, Object, object-detection, Object Detection, Object Reconstruction, RGBD Semantic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

In the realm of computer vision, the integration of advanced techniques into the processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth map as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color \textit{RGB} and depth \textit{D} channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation. The code and pre-trained models are publicly available at https://github.com/safouaneelg/FusionVision/.
Robust Policy Learning via Offline Skill Diffusion
Authors: Woo Kyung Kim, Minjong Yoo, Honguk Woo
Date: 2024-03-01 00:00:00
Tasks: Decoder, Imitation Learning, Reinforcement Learning (RL)

Skill-based reinforcement learning (RL) approaches have shown considerable promise, especially in solving long-horizon tasks via hierarchical structures. These skills, learned task-agnostically from offline datasets, can accelerate the policy learning process for new tasks. Yet, the application of these skills in different domains remains restricted due to their inherent dependency on the datasets, which poses a challenge when attempting to learn a skill-based policy via RL for a target domain different from the datasets' domains. In this paper, we present a novel offline skill learning framework DuSkill which employs a guided Diffusion model to generate versatile skills extended from the limited skills in datasets, thereby enhancing the robustness of policy learning for tasks in different domains. Specifically, we devise a guided diffusion-based skill decoder in conjunction with the hierarchical encoding to disentangle the skill embedding space into two distinct representations, one for encapsulating domain-invariant behaviors and the other for delineating the factors that induce domain variations in the behaviors. Our DuSkill framework enhances the diversity of skills learned offline, thus enabling to accelerate the learning procedure of high-level policies for different domains. Through experiments, we show that DuSkill outperforms other skill-based imitation learning and RL algorithms for several long-horizon tasks, demonstrating its benefits in few-shot imitation and online RL.