pwc-archive/papers-with-abstracts huggingface dataset:
Binary classification based Monte Carlo simulation
Authors: Elouan Argouarc'h, François Desbouvries
Date: 2023-07-29 00:00:00
Tasks: Binary Classification, Classification

Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve computing ratios of probability density functions (pdfs). On the other hand, classifiers discriminate labeled samples produced by a mixture of two distributions and can be used for approximating the ratio of the two corresponding pdfs.This bridge between simulation and classification enables us to propose pdf-free versions of pdf-ratio-based simulation algorithms, where the ratio is replaced by a surrogate function computed via a classifier. From a probabilistic modeling perspective, our procedure involves a structured energy based model which can easily be trained and is compatible with the classical samplers.
Non-parametric Hypothesis Tests for Distributional Group Symmetry
Authors: Kenny Chiu, Benjamin Bloem-Reddy
Date: 2023-07-28 00:00:00

Symmetry plays a central role in the sciences, machine learning, and statistics. For situations in which data are known to obey a symmetry, a multitude of methods that exploit symmetry have been developed. Statistical tests for the presence or absence of general group symmetry, however, are largely non-existent. This work formulates non-parametric hypothesis tests, based on a single independent and identically distributed sample, for distributional symmetry under a specified group. We provide a general formulation of tests for symmetry that apply to two broad settings. The first setting tests for the invariance of a marginal or joint distribution under the action of a compact group. Here, an asymptotically unbiased test only requires a computable metric on the space of probability distributions and the ability to sample uniformly random group elements. Building on this, we propose an easy-to-implement conditional Monte Carlo test and prove that it achieves exact $p$-values with finitely many observations and Monte Carlo samples. The second setting tests for the invariance or equivariance of a conditional distribution under the action of a locally compact group. We show that the test for conditional invariance or equivariance can be formulated as particular tests of conditional independence. We implement these tests from both settings using kernel methods and study them empirically on synthetic data. Finally, we apply them to testing for symmetry in geomagnetic satellite data and in two problems from high-energy particle physics.
Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce
Authors: Mayank Singh, Emily Ray, Marc Ferradou, Andrea Barraza-Urbina
Date: 2023-07-31 00:00:00
Tasks: Recommendation Systems

Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems. The assumption is to use a fixed Top-N to represent them. We propose that median impressions viewed from historical sessions per diner be used as a personalized value for N. We present preliminary exploratory results and list future steps to improve upon and evaluate the efficacy of these personalized metrics.
NEON: Living Needs Prediction System in Meituan
Authors: Xiaochong Lan, Chen Gao, Shiqi Wen, Xiuqi Chen, Yingge Che, Han Zhang, Huazhou Wei, Hengliang Luo, Yong Li
Date: 2023-07-31 00:00:00
Tasks: Prediction

Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect users to service providers, such as Meituan, the problem of living needs prediction is fundamental as it helps understand users and boost various downstream applications such as personalized recommendation. However, the problem has not been well explored and is faced with two critical challenges. First, the needs are naturally connected to specific locations and times, suffering from complex impacts from the spatiotemporal context. Second, there is a significant gap between users' actual living needs and their historical records on the platform. To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction. In the feature mining phase, we carefully extract individual-level user features for spatiotemporal modeling, and aggregated-level behavioral features for enriching data, which serve as the basis for addressing two challenges, respectively. Further, in the feature fusion phase, we propose a neural network that effectively fuses two parts of features into the user representation. Moreover, we design a multi-task prediction phase, where the auxiliary task of needs-meeting way prediction can enhance the modeling of spatiotemporal context. Extensive offline evaluations verify that our NEON system can effectively predict users' living needs. Furthermore, we deploy NEON into Meituan's algorithm engine and evaluate how it enhances the three downstream prediction applications, via large-scale online A/B testing.
LP-MusicCaps: LLM-Based Pseudo Music Captioning
Authors: Seungheon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam
Date: 2023-07-31 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Music Captioning, TAG, Transfer Learning

Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of large volumes of musical data. Despite its importance, researchers face challenges due to the costly and time-consuming collection process of existing music-language datasets, which are limited in size. To address this data scarcity issue, we propose the use of large language models (LLMs) to artificially generate the description sentences from large-scale tag datasets. This results in approximately 2.2M captions paired with 0.5M audio clips. We term it Large Language Model based Pseudo music caption dataset, shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale music captioning dataset with various quantitative evaluation metrics used in the field of natural language processing as well as human evaluation. In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings. The results demonstrate that our proposed approach outperforms the supervised baseline model.
Time-Aware Item Weighting for the Next Basket Recommendations
Authors: Aleksey Romanov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov
Date: 2023-07-30 00:00:00
Tasks: Next-basket recommendation

In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and intervals into account. We provide experiments on three real-world datasets, and TAIW outperforms well-tuned state-of-the-art baselines for next-basket recommendations. In addition, we show the results of an ablation study and a case study of a few items.
Publish for Public: Improving Access of Public Libraries Users to Research Findings through Plain Language Summaries
Authors: Behrooz Rasuli
Date: 2023-07-30 00:00:00

Public libraries play a crucial role in disseminating knowledge to society. However, most of their users do not have the specialized knowledge to understand the new research findings. Providing plain language summaries (PLSs) in public libraries is a way to make the new research findings more accessible and understandable for the public. This article proposes a framework for providing PLSs as a new service in public libraries. Drawing from the literature on science and society, PLSs, and public libraries, a theoretical framework is developed. The findings suggest that public libraries can collect PLSs through different methods, such as professional teams, researchers, crowdsourcing, etc. Library newsletters, special publications, brochures, independent online databases, and social networks are among the most effective for making PLSs accessible to users. By proposing a framework for providing PLSs in public libraries, this study helps to bridge the gap between scientific research and the public.
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation
Authors: Andreea Iana, Goran Glavaš, Heiko Paulheim
Date: 2023-07-29 00:00:00
Tasks: News Recommendation

Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w.r.t. these aspects. This customization is achieved by ``hardcoding`` additional constraints into the NNR's architecture and/or training objectives: any change in the desired recommendation behavior thus requires retraining the model with a modified objective. This impedes widespread adoption of multi-aspect news recommenders. In this work, we introduce MANNeR, a modular framework for multi-aspect neural news recommendation that supports on-the-fly customization over individual aspects at inference time. With metric-based learning as its backbone, MANNeR learns aspect-specialized news encoders and then flexibly and linearly combines the resulting aspect-specific similarity scores into different ranking functions, alleviating the need for ranking function-specific retraining of the model. Extensive experimental results show that MANNeR consistently outperforms state-of-the-art NNRs on both standard content-based recommendation and single- and multi-aspect customization. Lastly, we validate that MANNeR's aspect-customization module is robust to language and domain transfer.
Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
Authors: Bin Liu, Qin Luo, Bang Wang
Date: 2023-07-29 00:00:00
Tasks: Collaborative Filtering, Information Retrieval, Retrieval

Learning contrastive representations from pairwise comparisons has achieved remarkable success in various fields, such as natural language processing, computer vision, and information retrieval. Collaborative filtering algorithms based on pairwise learning also rooted in this paradigm. A significant concern is the absence of labels for negative instances in implicit feedback data, which often results in the random selected negative instances contains false negatives and inevitably, biased embeddings. To address this issue, we introduce a novel correction method for sampling bias that yields a modified loss for pairwise learning called debiased pairwise loss (DPL). The key idea underlying DPL is to correct the biased probability estimates that result from false negatives, thereby correcting the gradients to approximate those of fully supervised data. The implementation of DPL only requires a small modification of the codes. Experimental studies on five public datasets validate the effectiveness of proposed learning method.
Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services
Authors: Max T. M. Ng, Hani S. Mahmassani, Ömer Verbas, Taner Cokyasar, Roman Engelhardt
Date: 2023-07-29 00:00:00
Tasks: Autonomous Vehicles

This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.
A Simulator for Twenty20 Cricket
Authors: Jack Davis, HARSHA PERERA AND TIM B. SWARTZ
Date: 2015-03-01 00:00:00
Proceeding: Australian & New Zealand Journal of Statistics 2015 3

This paper develops a Twenty20 cricket simulator for matches between sides belonging to the
International Cricket Council. As input, the simulator requires the probabilities of batting outcomes which are dependent on the batsman, the bowler, the number of overs consumed and
the number of wickets lost. The determination of batting probabilities is based on an amalgam
of standard classical estimation techniques and a hierarchical empirical Bayes approach where
the probabilities of batting outcomes borrow information from related scenarios. Initially, the
probabilities of batting outcomes are obtained for the first innings. In the second innings, the
target score obtained from the first innings affects the aggressiveness of batting during the second
innings. We use the target score to modify batting probabilities in the second innings simulation.
This gives rise to the suggestion that teams may not be adjusting their second innings batting
aggressiveness in an optimal way. The adequacy of the simulator is addressed through various
goodness-of-fit diagnostics.
MineralImage5k: A benchmark for zero-shot raw mineral visual recognition and description
Authors: Sergey Nesteruk, Julia Agafonova, Igor Pavlov, Maxim Gerasimov, Nikolay Latyshev, Denis Dimitrov, Andrey Kuznetsov, Artur Kadurin, Pavel Plechov
Date: 2023-07-20 00:00:00
Proceeding: Computers and Geosciences 2023 7
Tasks: zero-shot-classification, Zero-Shot Learning

Mineral image recognition is a challenging computer vision problem. Without external tools, even a human expert cannot distinguish some mineral species accurately. Previous research was mainly focused on processed mineral recognition. This is considered to be a simplified statement of a problem because processed minerals are more visually expressive. On the contrary, in a raw sample, the target mineral can appear in the form of thinly represented inclusions. In real life, the raw samples usually require automatic mineral species identification.
Another difficulty in raw mineral recognition is the shortage of publicly available training and validation data. It is impossible to compare different deep learning approaches when the results are evaluated on dissimilar data.
The main contribution of this paper is providing an open benchmark for zero-shot raw mineral visual recognition. Besides the evaluation-only zero-shot classification dataset, we publish subsets for segmentation, mineral size estimation, and few-shot classification. For all of the provided computer vision problems, we publish baseline solutions we offer for the community to beat.
Deep adaptive hiding network for image hiding using attentive frequency extraction and gradual depth extraction
Authors: Le Zhang, Yao Lu, Jinxing Li, Fanglin Chen, Guangming Lu, David Zhang
Date: 2023-01-28 00:00:00
Proceeding: Neural Computing and Applications 2023 1
Tasks: Image Steganography, Steganographics

Image hiding secures information security in multimedia communication. Existing deep image hiding methods usually process the secret and cover information at first, and then fuse such entire processed information. This complete and rough fusion pipeline, however, severely hinders the quality improvement of the stego and revealed secret images. This paper proposes a deep image hiding architecture, named Deep Adaptive Hiding Network (DAH-Net), to gradually extract and fuse the necessary secret and cover information at the frequency and the depth (layer) extents. Specifically, we propose the Attentive Frequency Extraction method for the DAH-Net to adaptively extract the necessary secret and cover information at the frequency level. The Gradual Depth Extraction method is further proposed for the DAH-Net to gradually extract and fuse the attentive frequency secret and cover information at the depth (layer) level of the deep image hiding network. Extensive experiment results demonstrate the proposed DAH-Net is more universal and achieves state-of-the-art performances in image hiding, watermarking, and photographic steganography.
Joint adjustment image steganography networks
Authors: Le Zhang, Yao Lu, Tong Li, Guangming Lu
Date: 2023-07-15 00:00:00
Proceeding: Signal Processing: Image Communication 2023 7
Tasks: Image Steganography, Steganographics

Image steganography aims to achieve covert communication between two partners utilizing stego images generated by hiding secret images within cover images. Existing deep image steganography methods have been rapidly developed in this area. Such methods, however, usually generate the stego images and reveal the secret images using one-process networks, lacking sufficient refinement in these methods. Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography. This paper proposes Joint Adjustment Image Steganography Networks (JAIS-Nets), containing a series of coarse-to-fine iterative adjustment processes, for image steganography. Our JAIS-Nets first proposes Cross-Process Contrastive Refinement (CPCR) adjustment method, using the cross-process contrastive information from cover-stego and secret-revealed secret image pairs, to iteratively refine the generated stego and revealed secret images, respectively. In addition, our JAIS-Nets further proposes Cross-Process Multi-Scale (CPMS) adjustment method, using the cross-process multi-scale information from different scales cover-stego and secret-revealed secret image pairs, to directly adjust and enhance the intermediate representations of the proposed JAIS-Nets. Integrating the proposed CPCR with CPMS methods, the proposed JAIS-Nets can jointly adjust the quality of the stego and revealed secret images at both the learning process and image scale levels. Extensive experiments demonstrate that our JAIS-Nets can achieve state-of-the-art performances on the security and quality of the stego and revealed secret images on both the regular and large capacity image steganography.
Deep residual network for steganalysis of digital images
Authors: Mehdi Boroumand; Mo Chen; Jessica Fridrich
Date: 2018-09-23 00:00:00
Proceeding: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018 9
Tasks: Image Steganography, Quantization, Steganalysis, Steganographics

Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm - classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.
Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads
Authors: Ananthan Nambiar, Chao Pan, Vishal Rana, Mahdi Cheraghchi, João Ribeiro, Sergei Maslov, Olgica Milenkovic
Date: 2023-07-31 00:00:00

Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold ($Ct$) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic $Ct$ threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by $24$% while maintaining a negligible false negative rate.
Pathloss-based non-Line-of-Sight Identification in an Indoor Environment: An Experimental Study
Authors: Muhammad Asim, Muhammad Ozair Iqbal, Waqas Aman, Muhammad Mahboob Ur Rahman, Qammer H. Abbasi
Date: 2023-07-29 00:00:00

This paper reports the findings of an experimental study on the problem of line-of-sight (LOS)/non-line-of-sight (NLOS) classification in an indoor environment. Specifically, we deploy a pair of NI 2901 USRP software-defined radios (SDR) in a large hall. The transmit SDR emits an unmodulated tone of frequency 10 KHz, on a center frequency of 2.4 GHz, using three different signal-to-noise ratios (SNR). The receive SDR constructs a dataset of pathloss measurements from the received signal as it moves across 15 equi-spaced positions on a 1D grid (for both LOS and NLOS scenarios). We utilize our custom dataset to estimate the pathloss parameters (i.e., pathloss exponent) using the least-squares method, and later, utilize the parameterized pathloss model to construct a binary hypothesis test for NLOS identification. Further, noting that the pathloss measurements slightly deviate from Gaussian distribution, we feed our custom dataset to four machine learning (ML) algorithms, i.e., linear support vector machine (SVM) and radial basis function SVM (RBF-SVM), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). It turns out that the performance of the ML algorithms is only slightly superior to the Neyman-Pearson-based binary hypothesis test (BHT). That is, the RBF-SVM classifier (the best performing ML classifier) and the BHT achieve a maximum accuracy of 88.24% and 87.46% for low SNR, 83.91% and 81.21% for medium SNR, and 87.38% and 86.65% for high SNR.