Reviewerly/exHarmony huggingface dataset:
Arabic Named Entity Recognition Using Clustered Word Embedding

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement.
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer has been on the rise. However, the quadratic computational cost of self-attention has become a serious problem in practice applications. There has been much research on architectures without CNN and self-attention in this context. In particular, MLP-Mixer is a simple architecture designed using MLPs and hit an accuracy comparable to the Vision Transformer. However, the only inductive bias in this architecture is the embedding of tokens. This leaves open the possibility of incorporating a non-convolutional (or non-local) inductive bias into the architecture, so we used two simple ideas to incorporate inductive bias into the MLP-Mixer while taking advantage of its ability to capture global correlations. A way is to divide the token-mixing block vertically and horizontally. Another way is to make spatial correlations denser among some channels of token-mixing. With this approach, we were able to improve the accuracy of the MLP-Mixer while reducing its parameters and computational complexity. The small model that is RaftMLP-S is comparable to the state-of-the-art global MLP-based model in terms of parameters and efficiency per calculation. Our source code is available at https://github.com/okojoalg/raft-mlp .
Exploring Adversarially Robust Training for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have been shown to be vulnerable to adversarial attacks. However, very little focus is devoted to improving the adversarial robustness of deep UDA models, causing serious concerns about model reliability. Adversarial Training (AT) has been considered to be the most successful adversarial defense approach. Nevertheless, conventional AT requires ground-truth labels to generate adversarial examples and train models, which limits its effectiveness in the unlabeled target domain. In this paper, we aim to explore AT to robustify UDA models: How to enhance the unlabeled data robustness via AT while learning domain-invariant features for UDA? To answer this question, we provide a systematic study into multiple AT variants that can potentially be applied to UDA. Moreover, we propose a novel Adversarially Robust Training method for UDA accordingly, referred to as ARTUDA. Extensive experiments on multiple adversarial attacks and UDA benchmarks show that ARTUDA consistently improves the adversarial robustness of UDA models. Code is available at https://github.com/shaoyuanlo/ARTUDA .
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation

In 3D medical image segmentation, small targets segmentation is crucial for diagnosis but still faces challenges. In this paper, we propose the Axis Projection Attention UNet, named APAUNet, for 3D medical image segmentation, especially for small targets. Considering the large proportion of the background in the 3D feature space, we introduce a projection strategy to project the 3D features into three orthogonal 2D planes to capture the contextual attention from different views. In this way, we can filter out the redundant feature information and mitigate the loss of critical information for small lesions in 3D scans. Then we utilize a dimension hybridization strategy to fuse the 3D features with attention from different axes and merge them by a weighted summation to adaptively learn the importance of different perspectives. Finally, in the APA Decoder, we concatenate both high and low resolution features in the 2D projection process, thereby obtaining more precise multi-scale information, which is vital for small lesion segmentation. Quantitative and qualitative experimental results on two public datasets (BTCV and MSD) demonstrate that our proposed APAUNet outperforms the other methods. Concretely, our APAUNet achieves an average dice score of 87.84 on BTCV, 84.48 on MSD-Liver and 69.13 on MSD-Pancreas, and significantly surpass the previous SOTA methods on small targets.
Morphology of Convolutional Neural Network with Diagonalized Pooling

In the paper, we introduce the structure of a convolutional neural network with diagonalized pooling (DiagPooling). Existing deterministic poolings, like max or Average, cause loss of some critical information, which may be helpful for a further classification task. To minimize that loss, we propose an algorithm that considers information from all values over the pooling window via eigendecomposition. Eigendecomposition changes the basis to one in which the matrix of the pooling window has a diagonal form. Then DiagPooling yields the greatest diagonal element of the matrix, which corresponds to the greatest eigenvalue of the pooling window. The main difference between our idea and classical or PCA pooling methods is a change of basis without returning to it. Also, we suggest combinations of diagonalized pooling with normalization methods that increase models’ performance compared to CNNs that use classical pooling layers.
Intelligibility of Highly Predictable Polish Target Words in Sentences Presented to Czech Readers

This contribution analyses the role of sentential context in reading intercomprehension both from an information-theoretic and an error-analytical perspective. The assumption is that not only cross-lingual similarity can influence the successful word disambiguation in an unknown but related foreign language, but also that predictability in context contributes to better intelligibility of the target items. Experimental data were gathered for 149 Polish sentences [1] with highly predictable target words in sentence final position presented to Czech readers in a web-based cloze translation task. Psycholinguistic research showed that predictably of words in context correlates with cognitive effort to process the information provided by the word and its surprisal [3]. Our hypothesis is that intelligibility of highly predictable words in sentential context of a related language also correlates with surprisal values obtained from statistical trigram language models. In order to establish a baseline, the individual words were also presented to Czech readers in a context-free translation experiment [4]. For the majority of the target words, an increase in correct translations is observable in context, as opposed to the results obtained without context. The overall correlations with surprisal are low, the highest being the joint surprisal of the Polish stimulus sentence. The error-analysis shows systematic patterns that are at least equally important intercomprehension factors, such as linguistic distance or morphological mismatches.
Assessing Effectiveness of the Tsunami Alert Messages Issued by NEAMTWS-TSPs: a case study from May 2nd, 2020 South Crete Earthquake Tsunami alert for Egypt

A tsunami induced by a shallow offshore earthquake of magnitude Mw=6.7 occurred south of the island of Crete (Greece) on May 2nd, 2020. The initial tsunami alert message (TAMs) received by the Egyptian National Tsunami Warning Focal Point for Egypt (i.e. Egyptian National Research Institute of Astronomy and Geophysics-NRIAG) was issued by the Geodynamic Institute of the National Observatory of Athens (NOA-HLNTWC), and was based on preliminary, rather inaccurate hypocenter and magnitude estimates. About 36 minutes after the earthquake, a follow-up message with an increased tsunami warning level was issued; the updated warning was motivated by a significant revision of earthquake source parameters estimates. The later message, however, was issued without taking into account the available observations from sea-level data. In this study we investigate the effectiveness and usefulness of the TAM messages received by NRIAG for the coastal areas of Egypt (including the issue time and the source parameters on which the messages are based), by cross-checking them against observed and modelled seismological and sea level data. Based on results from the critical review of the tsunami warning messages, disseminated by NOA-HLNTWC and other TSPs in the Eastern Mediterranean Sea and received by NRIAG (which is a TWFP for Egypt), a comprehensive revision of the tsunami early warning system tools and procedures seems urgently needed in the region. The active involvement of countries along the southern coast of the Mediterranean turns out to be crucial, as the analysis shows that tsunami warning can only be efficient with international cooperation on data (seismic and sea level) and procedures.
ck-FARM: An R package to discover big data associations for business intelligence

Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.
Two-stage label distribution learning with label-independent prediction based on label-specific features

Label distribution learning (LDL) explicitly models label ambiguity based on the degree to which each label describes an instance. Existing LDL algorithms typically consider all features shared by all class labels. However, irrelevant or redundant information in the feature space can degrade the performance of the model. In this study, a novel two-stage LDL algorithm with label-independent prediction based on label-specific features (TSLIP) is proposed. The first stage combines sparse feature selection and instance correlation constraints. The original high-dimensional input feature space is mapped to a derived low-dimensional label-independent prediction space. The mapping operation not only supports the extraction of label-specific features but also brings the derived space closer to the label distribution space. The second stage employs the Pearson coefficient to explore the label correlation to enhance the performance of the model. Subsequently, the label-independent predictions are normalized to obtain the final label distribution. Extensive experiments are conducted on twelve real-world datasets. The results of the seven measures demonstrate the superiority of our algorithm compared with several state-of-the-art methods.
Weighted error-output recurrent echo kernel state network for multi-step water level prediction

With development of information techniques in navigation and shipping, machine learning algorithms are applied in enhancing navigation safety. One of critical areas, which attracts lots of attention from scientists and researchers, is water level prediction. Although randomization-based algorithms obtain good performance in water level prediction, these algorithms still have their own limitations. For example, unstable prediction problem caused by random weights selection, and the error accumulation problem caused by the conventional recurrent algorithm. In this study, we combine three proposed approach with the conventional echo state network. Firstly, the Gaussian kernel method transforms the input features into a high-dimensional features, which in some extent improves the forecasting accuracy. Secondly, kernel reservoir states are proposed. It not only abandons the random selected weights, but it also makes hidden neurons to connect closely. Lastly, a novel weighted error-output recurrent multi-step algorithm is proposed. It uses previous forecasting errors to update the current output weights in order to overcome the error accumulation problem. Based on above three approaches, a multi-step water level prediction model is proposed called Weighted Error-output Recurrent Echo Kernel State Network (WER-EKSN). The experimental results and statistical analysis represent that our proposed model has better forecasting performance than other compared models. It not only has the superior ability in water level prediction, but it also provides the evidences for the management of transportation in water-land, such as flood protection, and management of ship route.
Deep embedded clustering with distribution consistency preservation for attributed networks

Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes and cluster nodes on representation vectors learned from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions in two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP.
An Experimental Survey of ASA on DL Classifiers Using Multi-dialect Arabic Texts

Social media services have become a place for people around the world to express their opinions, concerns, and thoughts about universal topics in their different spoken languages. In the Arabic language in general, there are fewer research contributions within the multi-dialect approach. Furthermore, in ASA (Arabic Sentiment Analysis), initiatives addressing the multi-dialects in the Arabic language compared to English are insufficient where the use of classification techniques and natural language processing is at a lower rate. As a result, this research focuses on analyzing sentiments in Arabic text and especially in different dialects within the Arabic language due to its high variation among social media users. This paper focuses on performing sentiment analysis on a multi-dialectical dataset by using several DL (Deep Learning) models: CNN (convolution Neural Network), LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and CNN-LSTM. We conducted several experiments using the sequential, attention-based, segregation-aggregation approach on the multi-dialect Arabic text and on the translated version of the dataset. We then compared the results to two benchmark datasets across two word and character sentiment levels. The results obtained throughout the experiments showcased an accuracy of 79% across LSTM on word level on the attention-based approach ASA as well as the accuracy of 86% across both CNN and CNN_LSTM character level ASA using the attention-based approach.
An adaptive balance optimization algorithm and its engineering application

The policy of balance between exploration capability and exploitation capability directly affects the solution performance of the meta-heuristic algorithm in a limited time. In order to better balance the exploration and exploitation capabilities of the algorithm and meet the solution requirements of complex real-world problems, the adaptive balance optimization algorithm (ABOA) is proposed in this paper. The algorithm consists of a global search phase (GSP) and a local search phase (LSP) and is controlled by a fixed parameter. ABOA not only considers the balance of exploration and exploitation capabilities of the algorithm throughout the whole iterative process but also focuses on the balance of exploration and exploitation in both GSP and LSP. The search in both phases is focused around the respective search centers from outside to inside. ABOA balances the exploration and exploitation capabilities of the algorithm throughout the search process by two adaptive policies: changing the search area and changing the search center. Fifty-two unconstrained benchmark test functions were employed to evaluate the performance of ABOA. The results of ABOA were compared with nine excellent optimization algorithms available in the literature. The statistical results and Friedman test showed that ABOA was significantly competitive. Finally, the results of the examined engineering design problems showed that ABOA can solve the constrained optimization problem better compared to other methods.
Development of a Python Framework (pyRadman) for QPE using radar and CML data at DWD

The daily life of everybody is affected by weather, especially by precipitation events. Climate projections indicate that the number and intensity of heavy rain events could increase in future. Therefore, the interest to improve QPE has increased rapidly, particularly for assurances, public infrastructure and flood risk management.Currently, the QPE is calculated by using the RADOLAN algorithm of Deutscher Wetterdienst. This algorithm combines the data of 17 weather radars and roughly 1,200 rain gauges in Germany by adjusting the radar reflectivity to the precipitation amount measured at the ground. The adjustment process is done every 10 minutes using the hourly total of radar and rain gauge data. Due to the rain gauge data delivery the adjustment process is delayed by 25 minutes. For this reason, short convective precipitation events can only be observed insufficiently.&amp;#160;&amp;#160;&amp;#160;Therefore, the RADOLAN algorithm has to be adapted to improve the QPE based on shorter data accumulation time and contemporary data delivery. One approach is to use almost real-time available data from the telecommunication network. Rainfall leads to attenuation of the signal level of commercial microwave links (CMLs). The path integral of the attenuation along one sender-receiver pair can be related to a certain precipitation amount. Germany is covered by several thousands (~130,000 in total) of CMLs, which can potentially be used to quantify rainfall events. Especially in urban areas the density of CMLs exceeds the density of meteorological networks rain gauges clearly. Therefore, it becomes possible to observe convective extreme weather events with higher temporal and spatial resolution.Within the project HoWa-PRO, the Deutscher Wetterdienst (DWD) collaborates with University of Augsburg, Karlsruhe Institute of Technology in Garmisch-Partenkirchen, and Ericsson. One of the first tasks is to set up a continuous data flow from Ericsson to DWD. To investigate different combinations of data sources and adjustment intervals, a fast, flexible and expandable software framework for combining and processing this data has to be developed.&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;We present first results of QPE after adjustment using different combinations of data e.g. CML+radar data and gauge+radar data. These results were analyzed and compared to show the potential of using opportunistic data from CMLs for radar adjustment.
Reconstructing solar irradiance over the period 1996-2022

Knowledge of solar irradiance variability is critical to Earth's climate models and understanding the solar influence on Earth's climate. Direct measurements of the Total Solar Irradiance (TSI) have only been available since 1978 from instruments onboard multiple missions. Different calibrations of the individual instruments make estimates of the possible long-term trend in the TSI still uncertain. Knowledge of the Solar Spectral Irradiance (SSI) is even more undetermined. Here we use the carefully reduced observations from the Rome Precision Solar Photometric Telescope (Rome/PSPT) and semi-empirical irradiance models to reconstruct solar irradiance variations over the period 1996-2022. Results are discussed with respect to direct measurements and existing composite reconstructions.
Iteration of B-spline surface based deflectometric method for discontinuous specular surface

There is an over-growing demand for accurate three-dimension model of industrial components. In the measurement of specular surface, deflectometry has been widely used for its convenience and high accuracy. However, it is a challenge to measure discontinuous specular surface by the traditional deflectometry. In order to solve this problem, this paper proposes a deflectometric method based on iteration of control points of B-spline surface. In this method, a screen at two positions displays fringe patterns which will be reflected by specular surface under test and captured by a camera. According to the undistorted and absolute unwrapped phase map, a continuous zone containing all discontinuous area which is distinguished by a direct deflectometric method, is fitted by a B-spline surface with an estimated depth on the focus plane of the camera. Meanwhile, empty points and basis functions bridging isolated surface are removed. The control points of the fitted surface are solved by iterative calculation. With the solved control points, discontinuous specular surface is reconstructed. Simulated and actual experiments are carried out and the results show high accuracy and stability of the proposed method.
Coping with COVID-19 using contact tracing mobile apps

Purpose To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs. Design/methodology/approach Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents. Findings The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not. Originality/value This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.