pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions
Authors: Jie Yang, Yilin Li, Deddy Jobson
Date: 2022-07-23 00:00:00
Tasks: Decision Making

Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.
The influence of operating laser wavelengths on Doppler effect in LEO Optical satellite constellation
Authors: Kaouther Boumalek, Malek Benslama, Skander Aris
Date: 2021-06-28 00:00:00
Proceeding: 12th International Conference on Information and Communication Systems (ICICS) 2021 6

Nowadays, free space researches are focused on increasing the link's data transfer rate capacity. For this purpose, optical wavelengths are preferred over radio-frequency (RF) wavelengths as signal carriers to satisfy the high data transfer rate over huge distances since optical frequencies are greater than RF ones and a greater frequency implies better carrier capacity. Low earth orbit (LEO) satellite constellations with laser links are well suited to display a global broadband network. Since both LEO communicating satellites are in a continuous movement, the relative distance between them changes periodically. Doppler effect is detected on the received signal onboard terminal of the receiver satellite which shifts the characteristics of the received signal. In this paper, we study the influence of the operating wavelength on the Doppler effect in LEO inter-satellite laser links for full-time accessing inter-satellite links. The choice of the operating wavelength is an important task because Doppler wavelength shift is directly proportional to the operating wavelength. Results show that the lower laser operating wavelength the minimum Doppler wavelength shift appearance.
Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks
Authors: Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker
Date: 2021-07-09 00:00:00
Proceeding: IEEE Access 2021 7
Tasks: Classification

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively.
Bengali text document categorization based on very deep convolution neural network
Authors: Md. RajibHossaina, Mohammed MoshiulHoquea, NazmulSiddiqueb, Iqbal H.Sarker
Date: 2021-07-02 00:00:00
Proceeding: Expert Systems with Applications 2021 7
Tasks: Classification, Document Classification, text-classification, Text Classification

In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, search or manipulate. To manage such a massive number of documents effectively, an intelligent text document classification system is proposed in this paper. Intelligent classification of text document in a resource-constrained language (like Bengali) is challenging due to unavailability of linguistic resources, intelligent NLP tools, and larger text corpora. Moreover, Bengali texts are available in two morphological variants (i.e., Sadhu-bhasha and Cholito-bhasha) making the classification task more complicated. The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier. Due to the unavailability of standard corpus, this work develops a large Embedding Corpus (EC) containing 969,000 unlabelled texts and Bengali Text Classification Corpus (BDTC) containing 156,207 labelled documents arranged into 13 categories. Moreover, this work proposes the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali). Evaluation of 165 embedding models with intrinsic evaluators (semantic & syntactic similarity measures) shows that the GloVe model is more suitable (regarding Spearman & Pearson correlation) than other embeddings (Word2Vec, FastText, m-BERT) in Bengali text. Experimental results on the test dataset confirm that the proposed GloVe+VDCNN model outperformed (achieving the highest 96.96% accuracy) the other classification models and existing methods to perform the Bengali text classification task.
EOSC-LIFE WP4 TOOLBOX: Toolbox for sharing of sensitive data -- a concept description
Authors: Jan-Willem Boiten, Christian Ohmann, Ayodeji Adeniran, Steve Canham, Monica Cano Abadia, Gauthier Chassang, Maria Luisa Chiusano, Romain David, Maddalena Fratelli, Phil Gribbon, Petr Holub, Rebecca Ludwig, Michaela Th. Mayrhofer, Mihaela Matei, Arshiya Merchant, Maria Panagiotopoulou, Luca Pireddu, Alex Sanchez Pla, Irene Schlünder, George Tsamis, Harald Wagener
Date: 2022-07-28 00:00:00

The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing information to researchers who wish to share and/or use sensitive data in a cloud environment in general, and the European Open Science Cloud in particular. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox will provide links to recommendations, procedures, and best practices, as well as to software (tools) to support data sharing and reuse. It will be based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources. The current design document provides an outline for the anticipated toolbox, as well as its basic principles regarding content and sustainability.
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
Authors: Jonathan A. Sobel, Ronit Almog, Leo Anthony Celi, Michal Gaziel-Yablowitz, Danny Eytan, Joachim A. Behar
Date: 2022-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Prognosis

A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of patient physiology and of guide decision processes for diagnosis, prognosis and therapeutic interventions to improve care practice. Here, we reflect on the outcomes of an event that we organized in Israel at the end of March 2022 between the MIT Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel Institute of Technology (Technion) in Haifa. Participants were asked to complete a survey about their skills and interests, which enabled us to identify current needs in machine learning training for medical problem applications. This work describes opportunities and limitations in medical data science in the Israeli context.
EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM
Authors: Gayathri S, Akshat Anand, Astha Vijayvargiya, Pushpalatha M, Vaishnavi Moorthy, Sumit Kumar, Harichandana B S S
Date: 2022-07-12 00:00:00
Tasks: Emotion Recognition

Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.
Sixth-Order Compact Differencing with Staggered Boundary Schemes and 3(2) Bogacki-Shampine Pairs for Pricing Free-Boundary Options
Authors: Chinonso Nwankwo, Weizhong Dai
Date: 2022-07-28 00:00:00

We propose a stable sixth-order compact finite difference scheme with a dynamic fifth-order staggered boundary scheme and 3(2) R-K Bogacki and Shampine adaptive time stepping for pricing American style options. To locate, fix and compute the free-boundary simultaneously with option and delta sensitivity, we introduce a Landau transformation. Furthermore, we remove the convective term in the pricing model which could further introduce errors. Hence, an efficient sixth-order compact scheme can easily be implemented. The main challenge in coupling the sixth order compact scheme in discrete form is to efficiently account for the near-boundary scheme. In this work, we introduce novel fifth- and sixth-order Dirichlet near-boundary schemes suitable for solving our model. The optimal exercise boundary and other boundary values are approximated using a high-order analytical approximation obtained from a novel fifth-order staggered boundary scheme. Furthermore, we investigate the smoothness of the first and second derivatives of the optimal exercise boundary which is obtained from this high-order analytical approximation. Coupled with the 3(2) RK-Bogacki and Shampine time integration method, the interior values are then approximated using the sixth order compact operator. The expected convergence rate is obtained, and our present numerical scheme is very fast and gives highly accurate approximations with very coarse grids.
Open World Learning Graph Convolution for Latency Estimation in Routing Networks
Authors: Yifei Jin, Marios Daoutis, Sarunas Girdzijauskas, Aristides Gionis
Date: 2022-07-08 00:00:00
Tasks: Deep Learning

Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
Authors: Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro, Adam Gabrys, Jaime Lorenzo-Trueba
Date: 2022-07-29 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech, Voice Conversion

The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming. To overcome these issues, we demonstrate how to build low-resource, neural text-to-speech (TTS) voices with only 1 hour of conversational speech, when no other conversational data are available in the same language. Assuming the availability of non-expressive speech data in that language, we propose a 3-step technology: 1) we train an F0-conditioned voice conversion (VC) model as data augmentation technique; 2) we train an F0 predictor to control the conversational flavour of the voice-converted synthetic data; 3) we train a TTS system that consumes the augmented data. We prove that our technology enables F0 controllability, is scalable across speakers and languages and is competitive in terms of naturalness over a state-of-the-art baseline model, another augmented method which does not make use of F0 information.
Predicting Global HRTFs From Scanned Head Geometry Using Deep Learning and Compact Representations
Authors: Yuxiang Wang, You Zhang, Zhiyao Duan, Mark Bocko
Date: 2022-07-28 00:00:00

In the growing field of virtual auditory display, personalized head-related transfer functions (HRTFs) play a vital role in establishing an accurate sound image for mixed and augmented reality applications. In this work, we propose an HRTF personalization method employing convolutional neural networks (CNN) to predict a subject HRTFs for all directions from their scanned head geometry. To ease the training of the CNN models, we propose novel pre-processing methods for both the head scans and HRTF data to achieve compact representations. For the head scan, we use truncated spherical cap harmonic (SCH) coefficients to represent the pinna area, which is important in the acoustic scattering process. For the HRTF data, we use truncated spherical harmonic (SH) coefficients to represent the HRTF magnitudes and onsets. One CNN model is trained to predict the SH coefficients of the HRTF magnitudes from the SCH coefficients of the scanned ear geometry and other anthropometric measurements of the head. The other CNN model is trained to predict SH coefficients of the HRTF onsets from only the anthropometric measurements of the ear, head, and torso. Combining the magnitude and onset predictions, our method is able to predict the complete and global HRTF data. A leave-one-out validation with the log-spectral distortion (LSD) metric is used for objective evaluation. The results show a decent LSD level at both spatial \& temporal dimensions compared to the ground-truth HRTFs and a lower LSD than the boundary element method (BEM) simulation of HRTFs that the database provides. The localization simulation results with an auditory model are also consistent with the objective evaluation metrics, showing the localization responses with our predicted HRTFs are significantly better than with the BEM-calculated ones.
An Industrial Applicable Approach towards Design Optimization of a Mechanism: a Coronaventilator Case Study
Authors: Abdelmajid Ben Yahya, Nick Van Oosterwyck, Jan Herregodts, Stijn Herregodts, Simon Houwen, Bart Vanwalleghem, Stijn Derammelaere
Date: 2022-07-29 00:00:00

Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the potential described in the literature as these methods require too much theoretical analysis. This paper introduces a convenient optimization workflow allowing wide industrial adoption, by using CAD models. The 3D multi-body software is used to perform motion simulations, from which the objective value samples can be extracted. These motion simulations determine the required torque for a certain combination of design parameters to fulfill the movement. Dedicated software can execute multiple motion simulations sequentially and interchange data between the different simulations, which automates the process of retrieving objective value samples. Therefore, without in-depth analytical design analysis, a machine designer can evaluate multiple designs at low cost. Moreover, by implementing an optimization algorithm, an optimal design can be found that meets the objective. In a case study of a coronaventilator mechanism with three design parameters (DP's), 39 CAD motion simulations allowed to reduce the RMS torque of the mechanism by 57.2% in 42 minutes.
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data
Authors: Dennis Shen, Peng Ding, Jasjeet Sekhon, Bin Yu
Date: 2022-07-29 00:00:00
Tasks: regression, Time Series, Time Series Analysis

A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are observed over time. The information across time and space motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, and (ii) vertical regression (e.g., synthetic controls), which exploits cross-sectional patterns. Conventional wisdom states that the two approaches are fundamentally different. We establish this position to be partly false for estimation but generally true for inference. In particular, we prove that both approaches yield identical point estimates under several standard settings. For the same point estimate, however, each approach quantifies uncertainty with respect to a distinct estimand. In turn, the confidence interval developed for one estimand may have incorrect coverage for another. This emphasizes that the source of randomness that researchers assume has direct implications for the accuracy of inference.
Optimal Allocation of Limited Funds in Quadratic Funding
Authors: Ricardo A. Pasquini
Date: 2022-07-29 00:00:00

We examine the allocation of a limited pool of matching funds to public good projects using Quadratic Funding. In particular, we consider a variation of the Capital Constrained Quadratic Funding (CQF) mechanism proposed by Buterin, Hitzig and Weyl (2019) where only funds in the matching pool are distributed among projects. We show that this mechanism achieves a socially optimal allocation of limited funds.
The IPCC and the challenge of ex post policy evaluation
Authors: Richard S. J. Tol
Date: 2022-07-29 00:00:00

The IPCC started at a time when climate policy was an aspiration for the future. The research assessed in the early IPCC reports was necessarily about potential climate policies, always stylized and often optimized. The IPCC has continued on this path, even though there is now a considerable literature studying actual climate policy, in all its infuriating detail, warts and all. Four case studies suggest that the IPCC, in its current form, will not be able to successfully switch from ex ante to ex post policy evaluation. This transition is key as AR7 will most likely have to confront the failure to meet the 1.5K target. The four cases are as follows. (1) The scenarios first build and later endorsed by the IPCC all project a peaceful future with steady if not rapid economic growth everywhere, more closely resembling political manifestos than facts on the ground. (2) Successive IPCC reports have studiously avoided discussing the voluminous literature suggesting that political targets for greenhouse gas emission reduction are far from optimal, although a central part of that work was awarded the Nobel Prize in 2018. (3) IPCC AR5 found it impossible to acknowledge that the international climate policy negotiations from COP1 (Berlin) to COP19 (Warsaw) were bound to fail, just months before the radical overhaul at COP20 (Lima) proved that point. (4) IPCC AR6 by and large omitted the nascent literature on \textit{ex post} climate policy evaluation. Together, these cases suggest that the IPCC finds self-criticism difficult and is too close to policy makers to criticize past and current policy mistakes. One solution would be to move control over the IPCC to the national authorities on research and higher education.
Loss aversion in strategy-proof school-choice mechanisms
Authors: Vincent Meisner, Jonas von Wangenheim
Date: 2022-07-29 00:00:00

Evidence suggests that participants in strategy-proof matching mechanisms play dominated strategies. To explain the data, we introduce expectation-based loss aversion into a school-choice setting and characterize choice-acclimating personal equilibria. We find that non-truthful preference submissions can be strictly optimal if and only if they are top-rank monotone. In equilibrium, inefficiency or justified envy may arise in seemingly stable or efficient mechanisms. Specifically, students who are more loss averse or less confident than their peers obtain suboptimal allocations.