Reviewerly/exHarmony huggingface dataset:
Opportunities for <scp>eTextbook</scp> innovations and partnerships: A case study of the Marriott Library at the University of Utah

Key points Establishing business relationships with entities that can naturally and consistently refer others to your content, such as libraries and e‐book distributors, can further your reach and boost resiliency in times of crisis Many Utah professors were not aware of library‐licensing options for e‐books, but adoption of eBooks because of impacts from the COVID‐19 pandemic is likely to continue in the future Partnerships between academic librarians, campus bookstores, tutors, and even the Registrar are crucial to delivering agile responses to teaching needs A worryingly low number of teaching resources are available in digital form – and these are likely to be side‐lined where digital access becomes a requirement There are higher expectations around access and navigation of e‐books than printed versions, with frustration when they cannot be easily used Students appreciate lower costs and ease of access but are neither aware nor concerned about the different licensing agreements underpinning their access to materials.
Multi-Robot Path Planning for Cooperative 3D Printing

Abstract Cooperative 3D printing (C3DP) is an emerging technology that employs multiple mobile 3D printers to work together on printing the desired part cooperatively for better scalability and more flexibility. In our previous work, we developed a chunker, a geometric partition tool, to divide a part into smaller chunks and a computational framework to automatically generate schedules for collision-free printing. However, the framework can only generate print schedules for one print job. To schedule multiple robots to print multiple jobs, job assignment and path planning for robots to move between jobs also needs to be considered. In this paper, we apply the previously developed framework to generate print schedules for multiple jobs concurrently, and then developed a new approach to combine the independent print schedules with two heuristic methods: 1) Job by Job heuristics and 2) Row by Row heuristics. Once a single schedule is generated using one of the two heuristics, the conflict-based search algorithm is used to find the optimal path for robots to move from one print location to next. A case study is conducted where three different jobs are concurrently scheduled on a floor with four available printing robots. The results validate the utility of the proposed approach in generating viable print schedules with collision-free path planning.
Violation of Bell inequalities by stochastic simulations of Gaussian States based on their positive Wigner representation

Abstract At first sight, the use of an everywhere positive Wigner function as a probability density to perform stochastic simulations in quantum optics seems equivalent to the introduction of local hidden variables, thus preventing any violation of Bell inequalities. However, because of the difference between symmetrically and normally ordered operators, some trajectories in stochastic simulations can imply negative intensities, despite a positive mean. Hence, Bell inequalities do not apply. Here, we retrieve for a weakly squeezed Gaussian state the maximum violation on polarization states allowed by quantum mechanics, for the Clauser-Horn-Shimony-Holt (CHSH), as well as for the Clauser-Horn Bell inequalities. For the case of the Clauser-Horn Bell inequality, the influence of the quantum efficiency of the detectors is studied, and for both inequalities, the influence of the degree of squeezing is assessed, as well as the uncertainty range versus the number of trajectories used in the simulations.
Price Discovery in Bitcoin: The Impact of Unregulated Markets

We analyse minute-level multi-dimensional information flows within and between bitcoin spot and derivatives. We show that perpetual swaps and futures traded on the unregulated exchanges Huobi, OKEx and BitMEX are much the strongest instruments for bitcoin price discovery and we examine potential determinants of their leadership strength. Prices on the regulated CME bitcoin futures and the US-based spot exchanges react to, rather than lead, price movements on the unregulated exchanges and they may do so relatively slowly. In a multi-dimensional setting including the main price leaders within futures, perpetuals and spot markets, the CME futures have a very minor effect on price discovery, even less than the spot exchanges Bitfinex, Bitstamp and Coinbase. Our findings highlight the persistent problems stemming from inconsistent regulation in bitcoin spot and derivatives markets, including insufficient price stability and lack of resistance to manipulative trading. We conclude that the SEC are correct to maintain such issues as their main concern for bitcoin ETF applications.
A Quantum Algorithm for Linear PDEs Arising in Finance

We propose a hybrid quantum-classical algorithm, originated from quantum chemistry, to price European and Asian options in the Black-Scholes model. Our approach is based on the equivalence between the pricing partial differential equation and the Schrodinger equation in imaginary time. We devise a strategy to build a shallow quantum circuit approximation to this equation, only requiring few qubits. This constitutes a promising candidate for the application of Quantum Computing techniques (with large number of qubits affected by noise) in Quantitative Finance.
Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

Abstract The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.
Interpretable Models for Granger Causality Using Self-explaining Neural Networks

Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-causal effects and inspecting their variability over time. In comprehensive experiments on simulated data, we show that our framework performs on par with several powerful baseline methods at inferring Granger causality and that it achieves better performance at inferring interaction signs. The results suggest that our framework is a viable and more interpretable alternative to sparse-input neural networks for inferring Granger causality.
Intrinsic Decomposition of Document Images In-the-Wild.

Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to a significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 26% improvement of character error rate (CER), thus, proving the practical applicability.
Importance of Self-Consistency in Active Learning for Semantic Segmentation.

We address the task of active learning in the context of semantic segmentation and show that self-consistency can be a powerful source of self-supervision to greatly improve the performance of a data-driven model with access to only a small amount of labeled data. Self-consistency uses the simple observation that the results of semantic segmentation for a specific image should not change under transformations like horizontal flipping (i.e., the results should only be flipped). In other words, the output of a model should be consistent under equivariant transformations. The self-supervisory signal of self-consistency is particularly helpful during active learning since the model is prone to overfitting when there is only a small amount of labeled training data. In our proposed active learning framework, we iteratively extract small image patches that need to be labeled, by selecting image patches that have high uncertainty (high entropy) under equivariant transformations. We enforce pixel-wise self-consistency between the outputs of segmentation network for each image and its transformation (horizontally flipped) to utilize the rich self-supervisory information and reduce the uncertainty of the network. In this way, we are able to find the image patches over which the current model struggles the most to classify. By iteratively training over these difficult image patches, our experiments show that our active learning approach reaches $\sim96\%$ of the top performance of a model trained on all data, by using only $12\%$ of the total data on benchmark semantic segmentation datasets (e.g., CamVid and Cityscapes).
An Ameliorated hybrid model for Fraud Detection based on Tree based algorithms and Benford's Law

In today's living conditions amidst pandemic the online dependence of buying and selling is at its peak. The world globally is looking for safer and convenient options of using online payment facility. The online sales are roaring because of pandemic conditions around us and the fraudsters are actively pursuing evolving strategies for fraud in online payment. The main aim of this paper is to develop a novel algorithm using machine learning to learn fraudulent patterns in credit card transactions and apply it on test data. The Benford's Law is used to find if deviations occur in data and hence determines if fraud transactions are present in dataset. The unsupervised K means algorithm produces output in the form of two clusters consisting of Fraud and genuine transactions. The predicted data along with unseen data reserved in beginning are passed to Logistic Regression, Random Forest and XG Boost. The credit card transactions are applied to hybrid model which detects whether transaction is fraud or not. The experimental results demonstrate the effectiveness of the proposed model. The proposed hybrid method gives excellent results in Roc -AUC score and Fl-score as compared to simple Random Forest and Logistic Regression.
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness.

In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion of feature binding, which is defined as the process by which activation's spread across space and layers in the network are successfully integrated to arrive at a correct inference decision. In our work, this is accomplished for the task of dense image labelling by blending images based on their class labels, and then training a feature binding network, which simultaneously segments and separates the blended images. Subsequent feature denoising to suppress noisy activations reveals additional desirable properties and high degrees of successful predictions. Through this process, we reveal a general mechanism, distinct from any prior methods, for boosting the performance of the base segmentation network while simultaneously increasing robustness to adversarial attacks.
Extending Information Agreement by Continuity

Agreement measures are useful metrics to both compare different evaluations of the same diagnostic outcomes and validate new rating systems or devices. While many of them have been proposed in the literature so far, Cohen's n is still the de facto standard in gauging the agreement.Information Agreement (IA) is a novel two-observers information-theoretic-based metric introduced to overcome all the limitations and alleged pitfalls of Cohen's n. It offers an operative meaning to the agreement since it measures - in both dichotomous and multi-value ordered-categorical cases-the information shared between two raters through the virtual diagnostic channel connecting them: the more information exchanged between the raters, the higher their agreement. Unfortunately, this measure is only able to deal with agreement matrices whose values are all strictly positive numbers.This work extends IA by admitting also 0 as a possible value for the entries of an agreement matrix. Moreover, a Python software library to compute the extended version of IA, together with some of the most used agreement measures, is presented and tested.
Performance analysis of ASR Model for Santhali language on Kaldi and Matlab Toolkit

Speech recognition is the ability of devices to respond to spoken commands. In today's times when we are moving towards an automated world, the area of speech recognition has caught the eye of the researchers. The developments in this area are making waves all around us. Our proposed work presents a method to design a robust digit recognition system in Santhali language using Kaldi toolkit and MATLAB for small vocabulary dataset for varieties of features. Santhali is the most widely spoken local dialect in the state of Jharkhand. Using Kaldi, we trained our system with two training methods; monophone training and triphone training. The triphone method proves to be more efficient than the monophone method because of context mapping. In MATLAB, we obtained 95% accuracy for MFCC+LPC feature extraction applied to a GMM model.
ENHANCING RASA NLU MODEL FOR VIETNAMESE CHATBOT

Nowadays, the use of chatbots in industry and education has increased substantially. Building the chatbot system using traditional methods less effective than the applied of machine learning (ML) methods. Before chatbot based on finite-state, rule-base, knowledgebase, etc, but these methods still exist limitation. Recently, thanks to the advancement in natural language processing (NLP) and neural network (NN), conversational AI systems have made significant progress in many tasks such as intent classification, entity extraction, sentiment analysis, etc. In this paper, we implemented a Vietnamese chatbot for COVID-19 information that is capable of understanding natural language. It can generate responses, take actions to the user and remember the context of the conversation. We proposed Rasa platform for building chatbot and presented a method using custom pipeline for NLU model. In our work, we applied the pre-trained language models FastText and BERT and created custom tokenizer for our own pipelines. The application of pre-trained language models for NLU model has shown better results than the training model from scratch.
Research on Time Synchronization Technique of Multi-Source Composite Guidance Data Fusion

In the field of composite guidance, this paper analyzes three time synchronization methods for multi-source data fusion, establishes the standard measurement principle based on synchronization error, and compares the time synchronization effect and the measurement noise propagation result of the above three methods in terms of time slice and target maneuver. Simulation results indicate that the piece-wise linear interpolation method is the easiest method, has better inhibition effect on the uncertainty of noise propagation during time synchronization, is insensitive to time slice and target maneuver, and meets the requirements of multi-source composite guidance on time synchronization of data fusion.
A Benchmark for Inpainting of Clothing Images with Irregular Holes

Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image.
Entropic gradient descent algorithms and wide flat minima

The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, Entropy-SGD and Replicated-SGD, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet).
Research on Eco-geological Environment Carrying Capacity Based on GIS Technology

Abstract The environmental carrying capacity is also called environmental bearing capacity. The eco-geological environment carrying capacity is the limit of the environmental capacity to support human society and economic activities under a certain environmental condition in a certain period of time, combined with the factors of the ecological environment, the geological environment, and the socio-economic environment, it is an important basis for developing resources. Resources and the environment are the basis for human survival and development, with the development of society and the development of resources, the carrying capacity of resources and the environment in many areas has exceeded the limit, which has caused many environmental problems. Therefore, this paper studies the ecological environment carrying capacity based on GIS, hopes to provide some guidance for environmental protection.
Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce

Nowadays, live-stream and short video shopping in E-commerce have grown exponentially. However, the sellers are required to manually match images of the selling products to the timestamp of exhibition in the untrimmed video, resulting in a complicated process. To solve the problem, we present an innovative demonstration of multi-modal retrieval system called ``Fashion Focus'', which enables to exactly localize the product images in the online video as the focuses. Different modality contributes to the community localization, including visual content, linguistic features and interaction context are jointly investigated via presented multi-modal learning. Our system employs two procedures for analysis, including video content structuring and multi-modal retrieval, to automatically achieve accurate video-to-shop matching. Fashion Focus presents a unified framework that can orientate the consumers towards relevant product exhibitions during watching videos and help the sellers to effectively deliver the products over search and recommendation.
Differentiable Spatial Planning using Transformers

We consider the problem of spatial path planning. In contrast to the classical solutions which optimize a new plan from scratch and assume access to the full map with ground truth obstacle locations, we learn a planner from the data in a differentiable manner that allows us to leverage statistical regularities from past data. We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies, unlike prior data-driven planners that propagate information locally via convolutional structure in an iterative manner. In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs to in an end-to-end framework that has the structure of mapper and planner built into it which allows seamless generalization to out-of-distribution maps and goals. SPTs outperform prior state-of-the-art across all the setups for both manipulation and navigation tasks, leading to an absolute improvement of 7-19%.
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- the tensor algebra -- to capture such dependencies. To address the innate computational complexity of high degree tensors, we use compositions of low-rank tensor projections. This yields modular and scalable building blocks for neural networks that give state-of-the-art performance on standard benchmarks such as multivariate time series classification and generative models for video.