pwc-archive/papers-with-abstracts huggingface dataset:
On the Impact of Outlier Bias on User Clicks
Authors: Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke
Date: 2023-05-01 00:00:00
Tasks: counterfactual, Learning-To-Rank, Position

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, i.e., items at higher ranks are more likely to be examined and clicked. Inter-item dependencies also influence examination probabilities, with outlier items in a ranking as an important example. Outliers are defined as items that observably deviate from the rest and therefore stand out in the ranking. In this paper, we identify and introduce the bias brought about by outlier items: users tend to click more on outlier items and their close neighbors. To this end, we first conduct a controlled experiment to study the effect of outliers on user clicks. Next, to examine whether the findings from our controlled experiment generalize to naturalistic situations, we explore real-world click logs from an e-commerce platform. We show that, in both scenarios, users tend to click significantly more on outlier items than on non-outlier items in the same rankings. We show that this tendency holds for all positions, i.e., for any specific position, an item receives more interactions when presented as an outlier as opposed to a non-outlier item. We conclude from our analysis that the effect of outliers on clicks is a type of bias that should be addressed in ULTR. We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM). We estimate click propensities based on OPBM ; through extensive experiments performed on both real-world e-commerce data and semi-synthetic data, we verify the effectiveness of our outlier-aware click model. Our results show the superiority of OPBM against baselines in terms of ranking performance and true relevance estimation.
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Authors: Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen, Lizhen Cui, Zi Huang, Hongzhi Yin
Date: 2023-05-01 00:00:00
Tasks: Attribute, Conversational Recommendation, Recommendation Systems

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Despite the progress achieved in CRSs, existing solutions are far from satisfaction in the following two aspects: 1) current CRSs usually require each user to answer a quantity of clarifying questions before reaching the final recommendation, which harms the user experience; 2) there is a semantic gap between the learned representations of explicitly mentioned attributes and items. To address these drawbacks, we introduce the knowledge graph (KG) as the auxiliary information for comprehending and reasoning a user's preference, and propose a new CRS framework, namely Knowledge Enhanced Conversational Reasoning (KECR) system. As a user can reflect her/his preference via both attribute- and item-level expressions, KECR closes the semantic gap between two levels by embedding the structured knowledge in the KG. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user's feedback to clarifying questions. KECR can find a prominent reasoning chain to make the recommendation explainable and more rationale, as well as smoothen the conversation process, leading to better user experience and conversational recommendation accuracy. Extensive experiments on two real-world datasets demonstrate our approach's superiority over state-of-the-art baselines in both automatic evaluations and human judgments.
A Blueprint of IR Evaluation Integrating Task and User Characteristics: Test Collection and Evaluation Metrics
Authors: Kal Jarvelin, Eero Sormunen
Date: 2023-05-01 00:00:00

Relevance is generally understood as a multi-level and multi-dimensional relationship between an information need and an information object. However, traditional IR evaluation metrics naively assume mono-dimensionality. We ask: How to deal with multidimensional and graded relevance assessments in IR evaluation? Moreover, search result evaluation metrics neglect document overlaps and naively assume gains piling up as the searcher examines the ranked list into greater length. Consequently, we examine: How to deal with document overlap in IR evaluation? The usability of a document for a person-in-need also depends on document usability attributes beyond relevance. Therefore, we ask: How to deal with usability attributes, and how to combine this with multidimensional relevance assessments in IR evaluation? Finally, we ask how to define a formal model, which deals with multidimensional graded relevance assessments, document overlaps, and document usability attributes in a coherent framework serving IR evaluation?
Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research
Authors: Harshita Sahijwani, Kaustubh Dhole, Ankur Purwar, Venugopal Vasudevan, Eugene Agichtein
Date: 2023-04-30 00:00:00
Tasks: Marketing, Multiple-choice, Recommendation Systems

Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large. Such interviews generally consist of a series of questions that are asked to a participant. These interviews are typically conducted by skilled interviewers, who interpret the responses from the participants and can adapt the interview accordingly. Using automated conversational agents to conduct such interviews would enable reaching a much larger and potentially more diverse group of participants than currently possible. However, the technical challenges involved in building such a conversational system are relatively unexplored. To learn more about these challenges, we convert a market research multiple-choice questionnaire to a conversational format and conduct a user study. We address the key task of conducting structured interviews, namely interpreting the participant's response, for example, by matching it to one or more predefined options. Our findings can be applied to improve response interpretation for the information elicitation phase of conversational recommender systems.
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Authors: Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Gabriele Tolomei
Date: 2023-04-30 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Logical Reasoning, Recommendation Systems

Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security. In this work, we propose H-CARS, a novel strategy to poison recommender systems via CFs. Specifically, we first train a logical-reasoning-based surrogate model on training data derived from counterfactual explanations. By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model. Our experiments, which employ a well-known CF generation method and are conducted on two distinct datasets, show that H-CARS yields significant and successful attack performance.
Making Changes in Webpages Discoverable: A Change-Text Search Interface for Web Archives
Authors: Lesley Frew, Michael L. Nelson, Michele C. Weigle
Date: 2023-04-30 00:00:00

Webpages change over time, and web archives hold copies of historical versions of webpages. Users of web archives, such as journalists, want to find and view changes on webpages over time. However, the current search interfaces for web archives do not support this task. For the web archives that include a full-text search feature, multiple versions of the same webpage that match the search query are shown individually without enumerating changes, or are grouped together in a way that hides changes. We present a change text search engine that allows users to find changes in webpages. We describe the implementation of the search engine backend and frontend, including a tool that allows users to view the changes between two webpage versions in context as an animation. We evaluate the search engine with U.S. federal environmental webpages that changed between 2016 and 2020. The change text search results page can clearly show when terms and phrases were added or removed from webpages. The inverted index can also be queried to identify salient and frequently deleted terms in a corpus.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
Authors: Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He
Date: 2023-04-30 00:00:00
Tasks: Domain Generalization, In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Recommendation Systems

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.
Synthetic Cross-language Information Retrieval Training Data
Authors: James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, Scott Miller
Date: 2023-04-29 00:00:00
Tasks: Information Retrieval, Language Modelling, Large Language Model, Machine Translation, Retrieval, Translation

A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set led to significant advances in the state of the art in neural monolingual retrieval. By translating the MS MARCO documents into other languages using machine translation, this resource has been made useful to the CLIR community. Yet such translation suffers from a number of problems. While MS MARCO is a large resource, it is of fixed size; its genre and domain of discourse are fixed; and the translated documents are not written in the language of a native speaker of the language, but rather in translationese. To address these problems, we introduce the JH-POLO CLIR training set creation methodology. The approach begins by selecting a pair of non-English passages. A generative large language model is then used to produce an English query for which the first passage is relevant and the second passage is not relevant. By repeating this process, collections of arbitrary size can be created in the style of MS MARCO but using naturally-occurring documents in any desired genre and domain of discourse. This paper describes the methodology in detail, shows its use in creating new CLIR training sets, and describes experiments using the newly created training data.
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking
Authors: Chungang Zhuang, Shaofei Li, Han Ding
Date: 2023-08-10 00:00:00
Proceeding: Robotics and Computer-Integrated Manufacturing 2023 8
Tasks: 6D Pose Estimation, Instance Segmentation, Pose Estimation, Robotic Grasping, Semantic Segmentation

3D object pose estimation for robotic grasping and manipulation is a crucial task in the manufacturing industry. 
In cluttered and occluded scenes, the 6D pose estimation of the low-textured or textureless industrial object is a 
challenging problem due to the lack of color information. Thus, point cloud that is hardly affected by the lighting 
conditions is gaining popularity as an alternative solution for pose estimation. This article proposes a deep 
learning-based pose estimation using point cloud as input, which consists of instance segmentation and instance 
point cloud pose estimation. The instance segmentation divides the scene point cloud into multiple instance point 
clouds, and each instance point cloud pose is accurately predicted by fusing the depth and normal feature maps. 
In order to reduce the time consumption of the dataset acquisition and annotation, a physically-simulated engine 
is constructed to generate the synthetic dataset. Finally, several experiments are conducted on the public, synthetic and real datasets to verify the effectiveness of the pose estimation network. The experimental results show 
that the point cloud based pose estimation network can effectively and robustly predict the poses of objects in 
cluttered and occluded scenes.
Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net
Authors: Xiaolan Wang, Shuo Wang, Jiaqi Cao, Yansong Wang
Date: 2020-06-10 00:00:00
Proceeding: Wang, X., Wang, S., Cao, J. and Wang, Y., 2020. Data-driven based tiny-YOLOv3 method for front vehicle detection inducing SPP-net. IEEE Access, 8, pp.110227-110236. 2020 6
Tasks: vehicle detection

In order to solve the problem of low recognition rate and low real-time performance of vehicle detection in complex road environment, a data-driven forward vehicle detection algorithm based on improved tiny-YOLOv3 is proposed. Based on tiny-YOLOv3, the context feature information is combined to increase the two scale detections of tiny-YOLOv3 to three. The spatial pyramid pooling (SPP) module is added to increase the number of feature channels to improve the network feature extraction ability. According to the dense arrangement of vehicles on the horizontal axis in the road image ahead, we change the grid size of tiny-YOLOv3 and increase the number of candidate boxes on the horizontal axis. In addition, combined with the characteristics of the vehicle size in the road image ahead, K-means clustering method is used to select the appropriate number and size of target candidate boxes. We obtain the optimal detection model by multi-scale training of the improved network. The experimental results show that the average accuracy of the improved algorithm on the KITTI datasets is 91.03%, which is 7.12% higher than that of tiny-YOLOv3. And the detection speed of improved network is 144 frames/s, which meets the real-time requirements.
Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI
Authors: Cepeda S, Luppino LT, Pérez-Núñez A, Solheim O, García-García S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jiménez-Roldán L, Kuttner S
Date: 2023-02-16 00:00:00
Proceeding: Cancers 2023 2

The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.
InfoGAN-MSF: a data augmentation approach for correlative bridge monitoring factors
Authors: Ping Wan1, Hongli He2, Ling Guo1, Jiancheng Yang1 and Jie Li3, 2
Date: 2021-08-09 00:00:00
Proceeding: IOP Publishing Ltd 2021 8
Tasks: Data Augmentation

Bridge health evaluation has been a challenging issue due to high assessment errors with imbalanced and insufficient monitoring data of correlative bridge monitoring factors. We propose a data augmentation model as the preprocessing of bridge health evaluation to expand existing monitoring data of bridge monitoring factors on the basis of generative adversarial nets (GAN), named as information-GAN (InfoGAN)-multi-scale-filtering. In this model, new data of bridge monitoring factors are produced by the InfoGAN-based model with regards to learning coupling relations among bridge monitoring factors. To resolve generalization issues from the parameter matrix in traditional InfoGAN, we improve the discriminator with spectral normalization to optimize the weight training process. To deal with the instability of InfoGAN performance, which creates defective samples, a multi-scale filtering scheme is designed to obtain effective samples from the InfoGAN-based model. The scheme picks credible samples from both quantitative and qualitative aspects with the multiple scale filtering procedure. Additionally, inherent properties of bridge monitoring factors (e.g. distributions) are discovered within the generation process. Finally, filtered data are mixed into raw monitoring data to train classifiers. Simulation results imply that the proposed model performs effectively in data generation of real-world bridge monitoring factors and improves the performance of bridge health evaluation.
C3N：Content-Constrained Convolutional Network for Mural Image Completion
Authors: Xianlin Peng, Huayu Zhao, Xiaoyu Wang, Yongqin Zhang, Zhan Li, Qunxi Zhang, Jun Wang, Jinye Peng, Haida Liang
Date: 2022-12-06 00:00:00
Proceeding: Neural Computing and Applications 2022 12
Tasks: Image Inpainting, valid

Ancient murals, suffering from severe diseases, usually exhibit the absence or distortion of local areas. The damaged murals severely impaired people’s visual appreciation and satisfaction in the digital conservation of cultural heritage. However, there is no large amount of murals due to their scarcity. In this paper, we propose a novel content-constrained convolutional network for mural image completion. This method employs frequency transformation to facilitate effective multi-scale feature fusion for image inpainting, taking into account both space and frequency domains. Our network uses adaptive space-varying activation functions to correct feature maps across scales. Our network also uses dual-domain partial convolution with a mask for computing on only valid points, whereas the mask is updated for the next layer. This iterative process is performed until the mask is filled to build the repaired image. The proposed method is verified on the datasets in comparison with baseline methods. The experimental results demonstrate that the proposed method achieves better results with less artifacts in repairing mural images and generally outperforms the state-of-the-art methods both quantitatively and qualitatively. The code and pretrained models are available at https://github.com/zhangyongqin/C3N.
Cross-domain heterogeneous residual network for single image super-resolution
Authors: Li Ji, Qinghui Zhu, Yongqin Zhang, Juanjuan Yin, Ruyi Wei, Jinsheng Xiao, Deqiang Xiao, Guoying Zhao
Date: 2022-05-26 00:00:00
Proceeding: Neural Networks 2022 5
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction- and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance.