pwc-archive/papers-with-abstracts huggingface dataset:
Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results
Authors: Duleep Rathgamage Don, Ying Xie, Le Yu, Simon Hughes, Yun Zhu
Date: 2023-09-25 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval

This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model. The method aims to address the limitations of the bi-encoder architecture while maintaining a minimal additional training burden. The approach involves labeling top products for each query, generating semantically similar query clusters using the K-Means clustering algorithm, and fine-tuning a global language model into cluster language models on individual clusters. The parameters of each cluster language model are fine-tuned to learn local manifolds in the feature space efficiently, capturing the nuances of various query types within each cluster. The inference is performed by assigning a new query to its respective cluster and utilizing the corresponding cluster language model for retrieval. The proposed method results in more accurate and personalized retrieval results, offering a superior alternative to the popular bi-encoder based retrieval models in semantic search.
Sparsity-regularized coded ptychography for robust and efficient lensless microscopy on a chip
Authors: Ninghe Liu, Qianhao Zhao, Guoan Zheng
Date: 2023-09-24 00:00:00

Coded ptychography has emerged as a powerful technique for high-throughput, high-resolution lensless imaging. However, the trade-off between acquisition speed and image quality remains a significant challenge. To address this, we introduce a novel sparsity-regularized approach to coded ptychography that dramatically reduces the number of required measurements while maintaining high reconstruction quality. The reported approach, termed the ptychographic proximal total-variation (PPTV) solver, formulates the reconstruction task as a total variation regularized optimization problem. Unlike previous implementations that rely on specialized hardware or illumination schemes, PPTV integrates seamlessly into existing coded ptychography setups. Through comprehensive numerical simulations, we demonstrate that PPTV-driven coded ptychography can produce accurate reconstructions with as few as eight intensity measurements, a significant reduction compared to conventional methods. Convergence analysis confirms the robustness and stability of the PPTV algorithm. Experimental results from our optical prototype, featuring a disorder-engineered surface for wavefront modulation, validate PPTV's ability to achieve high-throughput, high-resolution imaging with a substantially reduced measurement burden. By enabling high-quality reconstructions from fewer measurements, PPTV paves the way for more compact, efficient, and cost-effective lensless microscopy systems on a chip, with potential applications in digital pathology, endoscopy, point-of-care diagnostics, and high-content screening.
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
Authors: Zihua Si, Zhongxiang Sun, Jiale Chen, Guozhang Chen, Xiaoxue Zang, Kai Zheng, Yang song, Xiao Zhang, Jun Xu, Kun Gai
Date: 2023-09-23 00:00:00
Tasks: Contrastive Learning, Recommendation Systems, Retrieval, Triplet

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.
mdendro: An R package for extended agglomerative hierarchical clustering
Authors: Alberto Fernández, Sergio Gómez
Date: 2023-09-23 00:00:00
Tasks: Clustering, Descriptive

"mdendro" is an R package that provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters at the same time, solving the nonuniqueness problem that arises when there are ties in the data. This problem causes that different binary dendrograms are possible depending both on the order of the input data and on the criterion used to break ties. Weighted and unweighted versions of the most common linkage methods are included in the package, which also implements two parametric linkage methods. In addition, package "mdendro" provides five descriptive measures to analyze the resulting dendrograms: cophenetic correlation coefficient, space distortion ratio, agglomeration coefficient, chaining coefficient and tree balance.
American Family Cohort, a data resource description
Authors: Deepa Balraj, Ayin Vala, Shiying Hao, Melanie Philofsky, Anna Tsvetkova, Elena Trach, Shravani Priya Narra, Oleg Zhuk, Mary Shamkhorskaya, Jim Singer, Joseph Mesterhazy, Somalee Datta, Isabella Chu, David Rehkopf
Date: 2023-09-22 00:00:00
Tasks: Diversity

This manuscript is a research resource description and presents a large and novel Electronic Health Records (EHR) data resource, American Family Cohort (AFC). The AFC data is derived from Centers for Medicare and Medicaid Services (CMS) certified American Board of Family Medicine (ABFM) PRIME registry. The PRIME registry is the largest national Qualified Clinical Data Registry (QCDR) for Primary Care. The data is converted to a popular common data model, the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The resource presents approximately 90 million encounters for 7.5 million patients. All 100% of the patients present age, gender, and address information, and 73% report race. Nealy 93% of patients have lab data in LOINC, 86% have medication data in RxNorm, 93% have diagnosis in SNOWMED and ICD, 81% have procedures in HCPCS or CPT, and 61% have insurance information. The richness, breadth, and diversity of this research accessible and research ready data is expected to accelerate observational studies in many diverse areas. We expect this resource to facilitate research in many years to come.
UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
Authors: Manuel Dibak, Vladimir Vlasov, Nour Karessli, Darya Dedik, Egor Malykh, Jacek Wasilewski, Ton Torres, Ana Peleteiro Ramallo
Date: 2023-09-18 00:00:00
Tasks: Recommendation Systems

Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience.
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Authors: Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Leijie Wang, Longqi Yang
Date: 2023-09-14 00:00:00

Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for emerging forms of Web search such as AI-driven chat. To understand user intents from log data, we need a way to label them with meaningful categories that capture their diversity and dynamics. Existing methods rely on manual or machine-learned labeling, which are either expensive or inflexible for large and dynamic datasets. We propose a novel solution using large language models (LLMs), which can generate rich and relevant concepts, descriptions, and examples for user intents. However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop. To address this, we propose a new methodology with human experts and assessors to verify the quality of the LLM-generated taxonomy. We also present an end-to-end pipeline that uses an LLM with human-in-the-loop to produce, refine, and apply labels for user intent analysis in log data. We demonstrate its effectiveness by uncovering new insights into user intents from search and chat logs from the Microsoft Bing commercial search engine. The proposed work's novelty stems from the method for generating purpose-driven user intent taxonomies with strong validation. This method not only helps remove methodological and practical bottlenecks from intent-focused research, but also provides a new framework for generating, validating, and applying other kinds of taxonomies in a scalable and adaptable way with reasonable human effort.
Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining
Authors: Sahar Voghoei, James Byars, John A Miller, Khaled Rasheed, Hamid A Arabnia
Date: 2023-09-06 00:00:00
Tasks: Classification

This paper proposes a model to predict the levels (e.g., Bachelor, Master, etc.) of postsecondary degree awards that have been ambiguously expressed in the student tracking reports of the National Student Clearinghouse (NSC). The model will be the hybrid of two modules. The first module interprets the relevant abbreviatory elements embedded in NSC reports by referring to a comprehensive database that we have made of nearly 950 abbreviations for degree titles used by American postsecondary educators. The second module is a combination of feature classification and text mining modeled with CNN-BiLSTM, which is preceded by several steps of heavy pre-processing. The model proposed in this paper was trained with four multi-label datasets of different grades of resolution and returned 97.83\% accuracy with the most sophisticated dataset. Such a thorough classification of degree levels will provide insights into the modeling patterns of student success and mobility. To date, such a classification strategy has not been attempted except using manual methods and simple text parsing logic.
Privacy Preserving Machine Learning for Behavioral Authentication Systems
Authors: Md Morshedul Islam, Md Abdur Rafiq
Date: 2023-08-31 00:00:00
Tasks: Privacy Preserving

A behavioral authentication (BA) system uses the behavioral characteristics of users to verify their identity claims. A BA verification algorithm can be constructed by training a neural network (NN) classifier on users' profiles. The trained NN model classifies the presented verification data, and if the classification matches the claimed identity, the verification algorithm accepts the claim. This classification-based approach removes the need to maintain a profile database. However, similar to other NN architectures, the NN classifier of the BA system is vulnerable to privacy attacks. To protect the privacy of training and test data used in an NN different techniques are widely used. In this paper, our focus is on a non-crypto-based approach, and we used random projection (RP) to ensure data privacy in an NN model. RP is a distance-preserving transformation based on a random matrix. Before sharing the profiles with the verifier, users will transform their profiles by RP and keep their matrices secret. To reduce the computation load in RP, we use sparse random projection, which is very effective for low-compute devices. Along with correctness and security properties, our system can ensure the changeability property of the BA system. We also introduce an ML-based privacy attack, and our proposed system is robust against this and other privacy and security attacks. We implemented our approach on three existing behavioral BA systems and achieved a below 2.0% FRR and a below 1.0% FAR rate. Moreover, the machine learning-based privacy attacker can only recover below 3.0% to 12.0% of features from a portion of the projected profiles. However, these recovered features are not sufficient to know details about the users' behavioral pattern or to be used in a subsequent attack. Our approach is general and can be used in other NN-based BA systems as well as in traditional biometric systems.
Heart Rate Extraction from Vowel Speech Signals
Authors: Abdelwadood Mesleh, Dmitriy Skopin, Sergey Baglikov, Anas Quteishat
Date: 2012-11-15 00:00:00
Proceeding: Journal of Computer Science and Technology 2012 11

This paper presents a novel non-contact heart rate extraction method from vowel speech signals. The proposed
method is based on modeling the relationship between speech production of vowel speech signals and heart activities for
humans where it is observed that the moment of heart beat causes a short increment (evolution) of vowel speech formants.
The short-time Fourier transform (STFT) is used to detect the formant maximum peaks so as to accurately estimate the
heart rate. Compared with traditional contact pulse oximeter, the average accuracy of the proposed non-contact heart rate
extraction method exceeds 95%. The proposed non-contact heart rate extraction method is expected to play an important
role in modern medical applications.
Region contrastive camera localization
Authors: Mehmet SARIGÜL, Levent Karacan
Date: 2023-05-01 00:00:00
Proceeding: Pattern Recognition Letters 2023 5
Tasks: Camera Localization, Camera Pose Estimation, Pose Estimation, Representation Learning

Visual camera localization is a well-studied computer vision problem and has many applications. Recently, deep convolutional neural networks have begun to be utilized to solve six-degree-of-freedom (6-DoF) camera pose estimation via scene coordinate regression from a single RGB image and they outperform the traditional methods. However, recent works do not consider scene variations such as viewpoint, light, scale, etc due to the camera motion. In this work, we propose a region contrastive representation learning approach to alleviate these problems. The proposed approach maps image features from different camera views of the same 3D region to nearby points in the learned feature space. In contrast, it pushes visual features of other regions to distant points. Our method improves the existing camera localization methods and achieves state-of-the-art results on indoor 7-Scenes and outdoor Cambridge Landmarks datasets. Experimental results show that the proposed approach reduces the pose and angle errors and increases the average accuracy from 84.8% to 85.62% on the state-of-the-art baseline model. In addition, we perform an ablation study on a baseline network with different settings to demonstrate the efficiency of the proposed region contrastive camera localization method.
Differential convolutional neural network
Authors: Mehmet SARIGÜL, Buse Melis ÖZYILDIRIM, Mutlu AVCI
Date: 2019-08-01 00:00:00
Proceeding: Neural Networks 2019 8

Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part. This inclusion directly increases the performance of artificial neural networks. This fact has led to the development of many different convolutional models and techniques.

In this work, a novel convolution technique named as Differential Convolution and updated error back-propagation algorithm is proposed. The proposed technique aims to transfer feature maps containing directional activation differences to the next layer. This implementation takes the idea of how convolved features change on the feature map into consideration. In a sense, this process adapts the mathematical differentiation operation into the convolutional process. Proposed improved back propagation algorithm also considers neighborhood activation errors. This property increases the classification performance without changing the number of filters.

Four different experiment sets were performed to observe the performance and the adaptability of the differential convolution technique. In the first experiment set utilization of the differential convolution on a traditional convolutional neural network structure made a performance boost up to 55.29% for the test accuracy. In the second experiment set differential convolution adaptation raised the top1 and top5 test accuracies of AlexNet by 5.3% and 4.75% on ImageNet dataset. In the third experiment set differential convolution utilized model outperformed all compared convolutional structures. In the fourth experiment set, the Differential VGGNet model obtained by adapting proposed differential convolution technique performed 93.58% and 75.06% accuracy values for CIFAR10 and CIFAR100 datasets, respectively. The accuracy values of the Differential NIN model containing differential convolution operation were 92.44% and 72.65% for the same datasets. In these experiment sets, it was observed that the differential convolution technique outperformed both traditional convolution and other compared convolution techniques. In addition, easy adaptation of the proposed technique to different convolutional structures and its efficiency demonstrate that popular deep learning models may be improved with differential convolution.
The Structural Biology and Critical Evaluation of Bacterial Proteases as Targets in New Drug Design
Authors: Maria Antony Dhivyan JE
Date: 2014-06-03 00:00:00
Tasks: Drug Design, Specificity

Bacteria produce a range of proteolytic enzymes, for which a number human equivalent or structurally similar examples exist and the primary focus of this study was to analyse the published literature to find proteolytic enzymes, specifically endoproteses and to examine the similarity in the substrates that they act on so as to predict a suitable structural motif which can be used as the basis for preparation of useful prodrug carriers against diseases caused by specific bacteria like Salmonella. Also, the similarities between the bacterial proteases and the action of human matrix metalloproteinases (MMPs), together with the MMP-like activity of bacterial endoproteases to activate human MMPs, were also analysed. This information was used to try to identify substrates on which the MMPs and bacterial proteases act, to aid the design of oligopeptide prodrug carriers to treat cancer and its metastatic spread. MMPs are greatly involved in cancer growth and progression, a few MMPs and certain proteases share a similar type of activity in degrading the extra cellular matrix (ECM) and substrates including gelatin. Our primary targets of study were to identify the proteases and MMPs that facilitate the migration of bacteria and growth of tumour cells respectively. The study was thus a two-way approach to study the substrate specificity of both bacterial proteases and MMPs, thereby to help in characterisation of their substrates. Various bioinformatics tools were used in the characterisation of the proteases and substrates as well as in the identification of possible binding sites and conserved regions in a range of candidate proteins. Central to this project was the salmonella derived PgtE surface protease that has been shown recently to act upon the pro-forms of human MMP-9.
Causal Discovery and Counterfactual Explanations for Personalized Student Learning
Authors: Bevan I. Smith
Date: 2023-09-18 00:00:00
Tasks: Causal Discovery, Causal Inference, counterfactual

The paper focuses on identifying the causes of student performance to provide personalized recommendations for improving pass rates. We introduce the need to move beyond predictive models and instead identify causal relationships. We propose using causal discovery techniques to achieve this. The study's main contributions include using causal discovery to identify causal predictors of student performance and applying counterfactual analysis to provide personalized recommendations. The paper describes the application of causal discovery methods, specifically the PC algorithm, to real-life student performance data. It addresses challenges such as sample size limitations and emphasizes the role of domain knowledge in causal discovery. The results reveal the identified causal relationships, such as the influence of earlier test grades and mathematical ability on final student performance. Limitations of this study include the reliance on domain expertise for accurate causal discovery, and the necessity of larger sample sizes for reliable results. The potential for incorrect causal structure estimations is acknowledged. A major challenge remains, which is the real-time implementation and validation of counterfactual recommendations. In conclusion, the paper demonstrates the value of causal discovery for understanding student performance and providing personalized recommendations. It highlights the challenges, benefits, and limitations of using causal inference in an educational context, setting the stage for future studies to further explore and refine these methods.