Reviewerly/exHarmony huggingface dataset:
Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport

Event Detection (ED) aims to identify mentions/triggers of real world events in text.In the literature, this task is modeled as a sequencelabeling or word-prediction problem.In this work, we present a novel formulation in which ED is modeled as a word-label alignment task.In particular, given the words in a sentence and possible event types, the objective is to infer an alignment matrix in which event trigger words are aligned with the most likely event types.Moreover, we show that this new perspective facilitates the incorporation of wordlabel alignment biases to improve alignment matrix for ED.Novel alignment biases and Optimal Transport are introduced to solve our alignment problem for ED.We conduct experiments on a benchmark dataset to demonstrate the effectiveness of the proposed model for ED.
Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator

Arabic is a morphologically rich and complex language, with numerous dialectal variants.Previous efforts on Arabic morphology modeling focused on specific variants and specific domains using a range of techniques with different degrees of linguistic modeling transparency.In this paper we propose a new approach to modeling Arabic morphology with an eye towards multi-dialectness, resource openness, and easy extensibility and use.We demonstrate our approach by modeling verbs from Standard Arabic and Egyptian Arabic, within a common framework, and with high coverage.
Entity Linking via Explicit Mention-Mention Coreference Modeling

Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions. In this paper, we present novel training and inference procedures that fully utilize mention-to-mention affinities by building minimum arborescences (i.e., directed spanning trees) over mentions and entities across documents in order to make linking decisions. We also show that this method gracefully extends to entity discovery, enabling the clustering of mentions that do not have an associated entity in the knowledge base. We evaluate our approach on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset, and show significant improvements in performance for both entity linking and discovery compared to identically parameterized models. We further show significant efficiency improvements with only a small loss in accuracy over previous work, which use more computationally expensive models.
Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua

The lack of resources for languages in the Americas has proven to be a problem for the creation of digital systems such as machine translation, search engines, chat bots, and more.The scarceness of digital resources for a language causes a higher impact on populations where the language is spoken by millions of people.We introduce the first official large combined corpus for deep learning of an indigenous South American low-resource language spoken by millions called Quechua.Specifically, our curated corpus is created from text gathered from the southern region of Peru where a dialect of Quechua is spoken that has not traditionally been used for digital systems as a target dialect in the past.In order to make our work repeatable by others, we also offer a public, pre-trained, BERT model called Qu-BERT which is the largest linguistic model ever trained for any Quechua type, not just the southern region dialect.We furthermore test our corpus and its corresponding BERT model on two major tasks: (1) named-entity recognition (NER) and (2) part-of-speech (POS) tagging by using state-of-the-art techniques where we achieve results comparable to other work on higher-resource languages.In this article, we describe the methodology, challenges, and results from the creation of QuBERT which is on on par with other state-of-the-art multilingual models for natural language processing achieving between 71 and 74% F1 score on NER and 84-87% on POS tasks.
Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases

Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors.In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects.The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency.We proposed a model that can simultaneously detect multiple brain diseases. The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value.
An Improved RRT<sup>*</sup> UAV Formation Path Planning Algorithm Based on Goal Bias and Node Rejection Strategy

This paper proposes an improved RRT * formation path planning algorithm based on goal bias and node rejection strategy to solve UAVs’ formation path planning problems in a complex environment. Aiming at the position constraint problem of multi-UAV in the planning process, the leader–follower structure among UAVs and the formation configuration model are established. Furthermore, aiming at node redundancy and slow planning speed caused by the RRT * algorithm in a complex environment, this paper sets the goal bias information so that the random tree could find the initial path quickly. At the same time, this paper proposes a node rejection strategy to prevent the nodes that do not meet the pre-set conditions from participating in the subsequent expansion. Compared to the standard RRT-related algorithms, the proposed improved algorithm can shorten the planning time, reduce the number of algorithm iterations and improve the algorithm’s applicability in the formation path planning problem.
Collaborative Decision-Making: A Proposal of an Semi-Automatic Facilitation Based on an Ontology

In this paper, we focus on a recommendation approach for collaborative decision-making. This recommendation is possible by designing an ontology in a first step. The construction of this ontology is based on a state of the art on collaborative decision-making, on ontology engineering and on collaboration engineering. An eight-step ontology development methodology was adopted and implemented to design the ontology. A corpus made up of one hundred and seventy-seven (177) documents was the starting point for the extraction of terms from the ontology and the Unified Modeling Language (UML) language is used as a description language of the ontology. This ontology is the starting point for a facilitation support system in a collaborative decision-making process. In a second step, we defined rules, based on our expertise on collaborative decision-making, used to guide a group of decision maker or a facilitator.
Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx &amp; PEDRO

Procedures aimed at explaining outcomes and behaviour of opaque predictors are becoming more and more essential as machine learning (ML) black-box (BB) models pervade a wide variety of fields and, in particular, critical ones - e.g., medical or financial -, where it is not possible to make decisions on the basis of a blind automatic prediction. A growing number of methods designed to overcome this BB limitation is present in the literature, however some ML tasks are nearly or completely neglected-e.g., regression and clustering. Furthermore, existing techniques may be not applicable in complex real-world scenarios or they can affect the output predictions with undesired artefacts. In this paper we present the design and the implementation of GridREx, a pedagogical algorithm to extract knowledge from black-box regressors, along with PEDRO, an optimisation procedure to automate the GridREx hyper-parameter tuning phase with better results than manual tuning. We also report the results of our experiments involving the application of GridREx and PEDRO in real case scenarios, including GridREx performance assessment by using as benchmarks other similar state-of-the-art techniques. GridREx proved to be able to give more concise explanations with higher fidelity and predictive capabilities.
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization

The increasingly common use of neural network classifiers in industrial and social applications of image analysis has allowed impressive progress these last years. Such methods are, however, sensitive to algorithmic bias, i.e., to an under- or an over-representation of positive predictions or to higher prediction errors in specific subgroups of images. We then introduce in this paper a new method to temper the algorithmic bias in Neural-Network-based classifiers. Our method is Neural-Network architecture agnostic and scales well to massive training sets of images. It indeed only overloads the loss function with a Wasserstein-2-based regularization term for which we back-propagate the impact of specific output predictions using a new model, based on the Gâteaux derivatives of the predictions distribution. This model is algorithmically reasonable and makes it possible to use our regularized loss with standard stochastic gradient-descent strategies. Its good behavior is assessed on the reference Adult census, MNIST, CelebA datasets.
A Dimensionality-Reducible Operational Optimal Control for Wastewater Treatment Process

Operational optimal control (OOC) is an essential component of wastewater treatment process (WWTP). The control variables usually are high-dimensional, nonlinear, and strongly coupled, which can easily fail traditional optimization control methods. Mathematically, these operational variables usually are in the unknown low-dimensional space embedded in the high-dimensional space. Therefore, the OOC problem of WWTP can be resolved as an optimization challenge involving low-dimensional space, and the unknown low-dimensional space is presented in the form of a set of controlled variables in a high-dimensional space, which is normal in real-world industries. Here, a dimension-reducible data-driven optimization control framework for WWTP is proposed. Considering the difficulty in elucidating the whole space of set points, a neural network is designed to approximate the constraint relationship between control variables. The search process is based on optimization methods in low-dimensional space embedded into Euclidean spaces. Furthermore, the convergence of the process is ensured via mathematical analysis. Finally, the experimental simulation of wastewater treatment revealed that this approach is effective for an optimal solution in control systems.
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland

Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting relevant processing workflows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to benefit the scientific community at large.
Exploring object and multi-target instrument tracking for AR-guided interventions

Abstract The rapid development of available hard- and software for computer-assisted or augmented reality (AR) guided interventions creates a need for fast and inexpensive prototyping environments. However, intraoperative tracking systems in particular represent a high cost threshold. Therefore, this work presents a low-cost tracking method based on a conventional RGB camera. Here, a combined approach of multiple image targets and 3D object target recognition is implemented. The system is evaluated with a systematic accuracy assessment analyzing a total of 385 3D positions. On average, a deviation of 15,69+-9,95 mm was measured. In addition, a prototypical AR-based needle navigation visualization was developed using Microsoft HoloLens 2. This system’s feasibility and usability was evaluated positively in a pilot study (n=3).
Digital Construction of Vocal Music Teaching Resource Base Using Data Mining Technology

Vocal music teaching resource databases can support teachers’ instruction and students’ learning while also enhancing the school’s ability to run a school because they are the main components of digital construction for vocal music teaching. Currently, system application is the main area of research for DM (data mining), which was previously focused on method discovery. A vocal music teaching resource base based on DM technology is created by using the theme as the central organising principle, processing, and organising the resources. Incorporate lesson planning, teaching, self-study, and output resource construction into a digital network resource application environment by using thematic resource development. Students’ achievement data, personal basic information, and evaluation data are used for DM based on the enhanced Apriori algorithm in order to uncover hidden rules and find correlations between various factors. This information is then used to support decision-making. When the system load is 1, the results demonstrate that the Apriori algorithm’s CPU waste rate and task success rate is 0.144 and 0.896, respectively. The study’s conclusion demonstrates that integrating DM technology into the field of education is theoretically and practically possible and that there is significant room for future study and application in this area.
Online Neuro Fuzzy Clustering of Data with Omissions and Outliers based on Сompletion Strategy

In the paper new recurrent adaptive algorithms for fuzzy clustering of data with missing values are proposed.This algorithm is based on fuzzy clustering procedures and self-learning Kohonen's rule using principle "Winner-Takes-More" with Cauchy neighborhood function.Using proposed approach it's possible to solve clustering task in on-line mode in situation when the amount of missing values in data is too big.
An Image Deblurring Method Using Improved U-Net Model

Deblurring methods in dynamic scenes are a challenging problem. Recently, significant progress has been made for image deblurring methods based on deep learning. However, these methods usually stack ordinary convolutional layers or increase convolution kernel size, resulting in limited receptive fields, an unsatisfying deblurring effect, and a heavy computational burden. Therefore, we propose an improved U-Net (U-shaped Convolutional Neural Network) model to restore the blurred images. We first design the model structure, which mainly includes depth-wise separable convolution, residual depth-wise separable convolution, wavelet transform, inverse wavelet transform, and a DMRFC (dense multireceptive field channel) module. Next, a depth-wise separable convolution is designed, which reduces model calculations and the number of parameters when compared with the standard convolution. A residual depth-wise separable convolution is designed, which allows for propagation of detailed information from different layers when compared with standard convolution and a standard residual block. The wavelet transform realizes downsampling by separating the contextual and texture information of the image. It also reduces model training difficulty. The inverse wavelet transform realizes upsampling, which reduces the loss of image information. Finally, by combining an extensional receptive field and channel attention mechanism, a DMRFC module is proposed to extract detailed image information, which further improves the reconstructed image quality via inverse wavelet transform. Experiments on the public dataset GOPRO show that the image deblurring method in this paper has higher-quality visual effects, while the PSNR (peak signal-to-noise ratio) rises to 30.83 and SSIM (structural similarity) rises to 0.948. Fewer model parameters and a shorter recovery time are needed, which provides a more lightweight image deblurring method.
A robust facemask forgery detection system in video

An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action.Deep-faking can also be used to represent images and sounds.We provide insights into our reviews in this document.We're showing our dataset to start.At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally.It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited).We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this.The edge groups are isolated from each video successively (without outline skips).The entire pipeline is ready to be finished when the approval stage is ten years old.Convolutional Neural Network (CNN) was the best and most reliable of the classification systems.Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena.'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face.In this study, we have implemented a sophisticated, knowledgeable method to recognize false images.Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure.
Automatic panoramic medical image stitching improvement based on feature-based approach

Clarification publications in the medical field are very important in making doctors the right decision by finding evidence to support his decision, therefore, the importance of collecting medical images and combining them with multiple overlapping areas of the same scene is important. This (processing, multimedia images and their medical applications) is very difficult. Our system proposed in this paper is applicable to the medical field of scoliosis and other Rib cage. The problem is the narrow vision of the X-ray machine and the lack of a large picture in one frame, the best solution is to combine more than one x-ray image into one panoramic image, our proposed method relies on in light of feature based methodology by Circle (Oriented-FAST and Rotated-BRIEF). The rapid wave approach is used to describe the feature through the use of BRIEF technology, the standard that has been adopted in our technology to describe the performance of the planning is based on the processing time and image quality created. The purpose of using the feature extraction approach in our technology is to obtain a high-resolution panoramic image plus short processing time, the results that we were able to obtain, according to the experimental results applied, resulted in ORB image quality and recording time.
PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications

Cloud computing is on the rise, which promotes new breeds of database systems to accommodate the cloud environment. The development of cloud-native databases reveals three trends. One is the adoption of multi-datacenter (DC) deployment to survive the downtime of any single site. Another is the separation of computation and storage resources to achieve higher elasticity and scalability. The last is the support of HTAP to eliminate data redundancy and system complexity from heterogeneous databases. To cater to these trends, we design a distributed relational database called PolarDB-X, which is built on top of the cloud-native database PolarDB. It hence inherits many cloud-native features, such as multi-datacenter deployment and elasticity. To achieve cross-DC capability, it leverages Paxos and hybrid logical clock to achieve durability and snapshot-isolation consistency with low coordination costs. For resource elasticity, since the underlying PolarDB supports rapid migration of tenants between nodes, PolarDB-X can quickly scale the cluster to cope with a sudden traffic increase. For HTAP support, with the help of read replicas and a HTAP executor, PolarDB-X can improve the latency and parallelism of analytical queries without impacting concurrently-running TP workloads. Using its MPP engine and an in-memory column index, the efficiency of analytical queries can be further enhanced. PolarDB-X is now a cloud database service at Alibaba Cloud. We have learned many useful lessons from its development and operation, and have incorporated those into our design and analysis.