Reviewerly/exHarmony huggingface dataset:
PIDFusion: Fusing Dense LiDAR Points and Camera Images at Pixel-Instance Level for 3D Object Detection

In driverless systems (scenarios such as subways, buses, trucks, etc.), multi-modal data fusion, such as light detection and ranging (LiDAR) points and camera images, is essential for accurate 3D object detection. In the fusion process, the information interaction between the modes is challenging due to the different coordinate systems of various sensors and the significant difference in the density of the collected data. It is necessary to fully consider the consistency and complementarity of multi-modal information, make up for the gap between multi-source data density, and achieve the joint interactive processing of multi-source information. Therefore, this paper is based on Transformer to improve a new multi-modal fusion model called PIDFusion for 3D object detection. Firstly, the method uses the results of 2D instance segmentation to generate dense 3D virtual points to enhance the original sparse 3D point clouds. This optimizes the issue that the nearest Euclidean distance in the 2D image space cannot ensure the nearest in the 3D space. Secondly, a new cross-modal fusion architecture is designed to maintain individual per-modality features to take advantage of their unique characteristics during 3D object detection. Finally, an instance-level fusion module is proposed to enhance semantic consistency through cross-modal feature interaction. Experiments show that PIDFusion is far ahead of existing 3D object detection methods, especially for small and long-range objects, with 70.8 mAP and 73.5 NDS on the nuScenes test set.
Json2Xlsx: Extraction and visualization of nested data in a sparse spreadsheet

Data extraction, visualization and distribution is an important task in the daily work of a database management team. In the medical field requests are often made by researchers who use spreadsheet programs such as Excel. Thus, we developed a framework which allows us to extract database tables with 1:N relations in a sparse spreadsheet which prevents redundant information. The Framework is implemented in PHP using a JSON interface. Further, we offer connectors for a direct connection to a Postgres database.
Interpretable Imitation Learning with Symbolic Rewards

Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world tasks as they naturally feature sparse rewards. In fact, this from-scratch approach is often impractical in environments where extreme negative outcomes are possible. Recent advances in imitation learning have improved sample efficiency by leveraging expert demonstrations. Most work along this line of research employs neural network-based approaches to recover an expert cost function. However, the complexity and lack of transparency make neural networks difficult to trust and deploy in the real world. In contrast, we present a method for extracting interpretable symbolic reward functions from expert data, which offers several advantages. First, the learned reward function can be parsed by a human to understand, verify and predict the behavior of the agent. Second, the reward function can be improved and modified by an expert. Finally, the structure of the reward function can be leveraged to extract explanations that encode richer domain knowledge than standard scalar rewards. To this end, we use an autoregressive recurrent neural network that generates hierarchical symbolic rewards represented by simple symbolic trees. The recurrent neural network is trained via risk-seeking policy gradients. We test our method in MuJoCo environments as well as a chemical plant simulator. We show that the discovered rewards can significantly accelerate the training process and achieve similar or better performance than neural network-based algorithms.
Layout-aware Single-image Document Flattening

Single image rectification of document deformation is a challenging task. Although some recent deep learning-based methods have attempted to solve this problem, they cannot achieve satisfactory results when dealing with document images with complex deformations. In this article, we propose a new efficient framework for document flattening. Our main insight is that most layout primitives in a document have rectangular outline shapes, making unwarping local layout primitives essentially homogeneous with unwarping the entire document. The former task is clearly more straightforward to solve than the latter due to the more consistent texture and relatively smooth deformation. On this basis, we propose a layout-aware deep model working in a divide-and-conquer manner. First, we employ a transformer-based segmentation module to obtain the layout information of the input document. Then a new regression module is applied to predict the global and local UV maps. Finally, we design an effective merging algorithm to correct the global prediction with local details. Both quantitative and qualitative experimental results demonstrate that our framework achieves favorable performance against state-of-the-art methods. In addition, the current publicly available document flattening datasets have limited 3D paper shapes without layout annotation and also lack a general geometric correction metric. Therefore, we build a new large-scale synthetic dataset by utilizing a fully automatic rendering method to generate deformed documents with diverse shapes and exact layout segmentation labels. We also propose a new geometric correction metric based on our paired document UV maps. Code and dataset will be released at https://github.com/BunnySoCrazy/LA-DocFlatten .
The Development and Teaching Application of Japanese Peripheral Language Phenomenon Based on Big Data Corpus

Since the introduction of the concept of big data corpus into language analysis, many scholars have conducted research on Japanese big data corpus, especially the subject, from different language views and perspectives, and have drawn many valuable conclusions. However, due to scholars' different theories and different thinking modes, their understanding of the Japanese big data corpus is still quite different. The seminar teaching method, as a teaching mode for the cultivation of innovative talents in Japanese peripheral languages, has distinctive features such as interactivity, democracy, motivation, and extension. It has been widely used in postgraduate and undergraduate courses at home and abroad and applied to A wide range of disciplines. Especially in Japan, each university has Seminar courses. Because of its strong practicality, it is conducive to cultivating students' independent learning ability, organization and coordination ability, and communication ability, so whether it can be applied and promoted in the teaching of Japanese peripheral languages, this research is carried out. Although experts have always been committed to the innovation of Japanese teaching, the advancement of teaching materials, and the continuous innovation of design, they also need the teaching practice and experimentation of front-line teachers. After years of practice in basic Japanese teaching by generations of teachers, we still encounter this problem: students cannot use the language fluently, they can only memorize words, grammar, and the usage of some fixed terms by rote; Some students with strong writing ability are not strong in conversation and expression. According to these problems in teaching, the author found that students could not use language well, probably because they did not have the awareness and desire to communicate. Sometimes the single teaching method and the boring teaching content make students uninterested in learning Japanese, and the large number of pseudonyms, Chinese characters, and complex grammatical structures make students dazzled. The experimental results show that the new teaching method proposed by the experimental model is suitable for the learning of Japanese peripheral languages, which improves the student's learning and thinking ability, which proves that the optimized NN-SLVM model is good.
Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

Photovoltaic (PV) power generation exhibits stochastic and uncertain characteristics. In order to improve the economy and reliability of a photovoltaic-energy storage system (PV-ESS), it is crucial to optimize both the energy storage capacity size and the charging and discharging strategies of the ESS. An optimal scheduling model for PV-ESS is proposed in this paper, comprehensively considering factors in terms of energy cost and charging/discharging constraints of the PV-ESS. Moreover, the model employs a particle swarm optimization-backpropagation (PSO-BP) neural network to predict the PV power using historical generation data from a factory in Xiamen. The proposed two PV-ESS scheduling strategies are compared under three weather conditions. In the demand management strategy, the ESS can flexibly respond to different weather conditions and load demand changes, and effectively reduce the electricity cost for users.
Design and Implementation of Multimedia Teaching Course for Piano Enlightenment Oriented to Aesthetic Ability Development

Piano education for children is based on the child's psychological characteristics and the artistic characteristics of musical works. Piano initiation education for young children can enhance perception, aesthetic ability, and creative skills; promote the overall development of children; and is also a crucial channel to promote young children to develop and transfer their own musical experience. This paper analyzes the current problem of cultivating children's musical aesthetic ability in piano initiation education and proposes the use of multimedia and artificial intelligence technology to compare the repertoire played by piano learners with the original repertoire, combined with aesthetic evaluation technology to analyze and give guidance for piano initiation practice, so that piano learners can be more targeted. The program is designed to solve the problems in the piano practice process, to enhance the musical perception and aesthetic feeling of children, and to improve their aesthetic ability.
Biased random access codes

A random access code (RAC) is a communication task in which the sender encodes a random message into a shorter one to be decoded by the receiver so that a randomly chosen character of the original message is recovered with some probability. Both the message and the character to be recovered are assumed to be uniformly distributed. In this paper, we extend this protocol by allowing more general distributions of these inputs, which alters the encoding and decoding strategies optimizing the protocol performance, with either classical or quantum resources. We approach the problem of optimizing the performance of these biased RACs with both numerical and analytical tools. On the numerical front, we present algorithms that allow a numerical evaluation of the optimal performance over both classical and quantum strategies and provide a Python package designed to implement them, called RAC-tools. We then use this numerical tool to investigate single-parameter families of biased RACs in the ${n}^{2}\ensuremath{\mapsto}1$ and ${2}^{d}\ensuremath{\mapsto}1$ scenarios. For RACs in the ${n}^{2}\ensuremath{\mapsto}1$ scenario, we derive a general upper bound for the cases in which the inputs are not correlated, which coincides with the quantum value for $n=2$ and in some cases for $n=3$. Moreover, it is shown that attaining this upper bound self-tests pairs or triples of rank-1 projective measurements, respectively. An analogous upper bound is derived for the value of RACs in the ${2}^{d}\ensuremath{\mapsto}1$ scenario, which is shown to be always attainable using mutually unbiased measurements if the distribution of input strings is unbiased.
Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network

Abstract Background Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of negation modifiers represents an important challenge. A wide range of cNLP applications have been developed to detect the negation of medical entities in clinical free-text, however, effective solutions for languages other than English are scarce. This study aimed at developing a solution for negation recognition in Spanish EHRs based on a combination of a customized rule-based NegEx layer and a convolutional neural network (CNN). Methods Based on our previous experience in real world evidence (RWE) studies using information embedded in EHRs, negation recognition was simplified into a binary problem (‘affirmative’ vs. ‘non-affirmative’ class). For the NegEx layer, negation rules were obtained from a publicly available Spanish corpus and enriched with custom ones, whereby the CNN binary classifier was trained on EHRs annotated for clinical named entities (cNEs) and negation markers by medical doctors. Results The proposed negation recognition pipeline obtained precision, recall, and F1-score of 0.93, 0.94, and 0.94 for the ‘affirmative’ class, and 0.86, 0.84, and 0.85 for the ‘non-affirmative’ class, respectively. To validate the generalization capabilities of our methodology, we applied the negation recognition pipeline on EHRs (6,710 cNEs) from a different data source distribution than the training corpus and obtained consistent performance metrics for the ‘affirmative’ and ‘non-affirmative’ class (0.95, 0.97, and 0.96; and 0.90, 0.83, and 0.86 for precision, recall, and F1-score, respectively). Lastly, we evaluated the pipeline against two publicly available Spanish negation corpora, the IULA and NUBes, obtaining state-of-the-art metrics (1.00, 0.99, and 0.99; and 1.00, 0.93, and 0.96 for precision, recall, and F1-score, respectively). Conclusion Negation recognition is a source of low precision in the retrieval of cNEs from EHRs’ free-text. Combining a customized rule-based NegEx layer with a CNN binary classifier outperformed many other current approaches. RWE studies highly benefit from the correct recognition of negation as it reduces false positive detections of cNE which otherwise would undoubtedly reduce the credibility of cNLP systems.
The Factors of E-Government Service Quality in Kuwait During the Coronavirus Disease 2019 Pandemic.

This study aims to explain factors that have influenced the e-government service quality in Kuwait during the Coronavirus Disease 2019 (COVID-19) pandemic. Data is collected data from 392 users of the e-government services in Kuwait. The analysis has revealed that having a functional e government portal allows citizens to complete their transactions and depend heavily on using the e-government services. Moreover, trust, ease of use, and citizen’s support were also significant in terms of affecting service quality. The findings also show reliability of content and appearance of information as significant factors. COVID-19 had moderating influence on the relationship between reliability, trust, and citizens’ support with e-government service quality. These results imply that citizens care about the functionality of the e-government portal and that it allows them to conduct all the transactions and processes that they need. These findings are useful for government officials to understand the most important elements for e-government service quality and thus design services.
How Far Have We Progressed in the Sampling Methods for Imbalanced Data Classification? An Empirical Study

Imbalanced data are ubiquitous in many real-world applications, and they have drawn a significant amount of attention in the field of data mining. A variety of methods have been proposed for imbalanced data classification, and data sampling methods are more prevalent due to their independence from classification algorithms. However, due to the increasing number of sampling methods, there is no consensus about which sampling method performs best, and contradictory conclusions have been obtained. Therefore, in the present study, we conducted an extensive comparison of 16 different sampling methods with four popular classification algorithms, using 75 imbalanced binary datasets from several different application domains. In addition, four widely-used measures were employed to evaluate the corresponding classification performance. The experimental results showed that none of the employed sampling methods performed the best and stably across all the used classification algorithms and evaluation measures. Furthermore, we also found that the performance of the different sampling methods was usually affected by the classification algorithms employed. Therefore, it is important for practitioners and researchers to simultaneously select appropriate sampling methods and classification algorithms, for handling the imbalanced data problems at hand.
A Hyperbolic Secant-Squared Distribution via the Nonlinear Evolution Equation and Its Application

In this article, we present a hyperbolic secant-squared distribution via the nonlinear evolution equation. Namely, for this equation, the probability density function of the hyperbolic secant-squared (HSS) distribution has been determined. The density of our model has a variety of shapes, including symmetric, left-skewed, and right-skewed. Eight distinct frequent list estimation methods have been proposed for estimating the parameters of our models. Additionally, these estimation techniques have been used to examine the behavior of the HSS model parameters using data sets that were generated randomly. To demonstrate how the findings may be used to model real data using the HSS distribution, we also use real data. Finally, the proposed justification can be applied to a variety of other complex physical models.
Energy-Efficient Blockchain-Enabled Multi-Robot Coordination for Information Gathering: Theory and Experiments

In this work, we propose a blockchain-based solution for securing robot-to-robot communication for a task with a high socioeconomic impact—information gathering. The objective of the robots is to gather maximal information about an unknown ambient phenomenon such as soil humidity distribution in a field. More specifically, we use the proof-of-work (PoW) consensus protocol for the robots to securely coordinate while rejecting tampered data injected by a malicious entity. As the blockchain-based PoW protocol has a large energy footprint, we next employ an algorithmically-engineered energy-efficient version of PoW. Results show that our proposed energy-efficient PoW-based protocol can reduce energy consumption by 14% while easily scaling up to 10 robots.
Financial inclusion and inclusive growth in Africa: What is the moderation role of financial stability?

This article aims to explore the interplay between financial stability, financial inclusion, and inclusive growth in 40 African countries during the period 2004–2020. It acknowledges that an unstable financial system has the potential to erode confidence and hinder the essence of financial inclusion in promoting inclusive growth. However, studies regarding the combined effect of financial inclusion and financial stability on inclusive growth are hard to find, especially in Africa. By examining the effects of financial inclusion on inclusive growth and the synergistic relationship between financial stability and inclusive growth, this study seeks to shed light on how these factors interact in the context of African economies. To cater for endogeneity issues, we used a two-step system generalized method of moment. Our result reveals three outcomes: First, financial inclusion promotes inclusive growth. Second, financial stability alone is less effective to enhance inclusive growth. Lastly, financial stability forms synergy with financial inclusion to further spike inclusive growth. It is recommended that policymakers should strive to enhance financial inclusion by promoting financial stability.
Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation

Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.
Exploring the performance of implicit neural representations for brain image registration

Abstract Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of brain image registration in magnetic resonance imaging. In this setting, INRs serve as a continuous and coordinate based approximation of the deformation field obtained through a multi-layer perceptron. Previous research has demonstrated that sinusoidal representation networks (SIRENs) surpass ReLU models in performance. In this study, we first broaden the range of activation functions to further investigate the registration performance of implicit networks equipped with activation functions that exhibit diverse oscillatory properties. Specifically, in addition to the SIRENs and ReLU, we evaluate activation functions based on snake, sine+, chirp and Morlet wavelet functions. Second, we conduct experiments to relate the hyper-parameters of the models to registration performance. Third, we propose and assess various techniques, including cycle consistency loss, ensembles and cascades of implicit networks, as well as a combined image fusion and registration objective, to enhance the performance of implicit registration networks beyond the standard approach. The investigated implicit methods are compared to the VoxelMorph convolutional neural network and to the symmetric image normalization (SyN) registration algorithm from the Advanced Normalization Tools (ANTs). Our findings not only highlight the remarkable capabilities of implicit networks in addressing pairwise image registration challenges, but also showcase their potential as a powerful and versatile off-the-shelf tool in the fields of neuroscience and radiology.