Reviewerly/exHarmony huggingface dataset:
Dense fuzzy support vector machine to binary classification for imbalanced data

Majority classes are easily to be found in imbalance datasets, instead, minority classes are hard to be paid attention to due to the number of is rare. However, most existing classifiers are better at exploring majority classes, resulting in that classification results are unfair. To address this issue of binary classification for imbalance data, this paper proposes a novel fuzzy support vector machine. The thought is that we trained two support vector machines to learn the majority class and the minority class, respectively. Then, the proposed fuzzy is used to estimate the assistance provided by instance points for the training of the support vector machines. Finally, it can be judged for unknown instance points through evaluating that they provided the assistance to the training of the support vector machines. Results on the ten UCI datasets show that the class accuracy of the proposed method is 0.747 when the imbalanced ratio between the classes reaches 87.8. Compare with the competitors, the proposed method wins over them in classification performance. We find that aiming at the classification of imbalanced data, the complexity of data distribution has negative effects on classification results, while fuzzy can resist these negative effects. Moreover, fuzzy can assist those classifiers to gain superior classification boundaries.
Investigating Students’ Perceptions on using Tech-books in the classrooms

Technology such as iPads has been increasingly popular in classrooms in recent years. Learners are gradually moving away from textbooks and toward digital books on iPads. This study lists the pros of learning with iPad learning. Furthermore, it deals with the benefits of using i Pads in the classroom from the students' perspective. To collect data and information for this study, the researchers used both a quantitative and qualitative technique. This qualitative study chose to use a questionnaire that was delivered to the target audience of Qassim university students. The data was then descriptively reviewed, and the majority of those engaged in this survey claimed that they preferred using an iPad in the classroom since it was more convenient and easier to use if a student wants to look for certain information. Nonetheless, a few respondents disagreed and wanted to use the textbook and learn in the usual manner. Some educators, on the other hand, believe it is an elephant in the room. However, I Pads are a solution for all of the untold sufferings of students and would usher us into the ultra digital era if this facility is provided to one and all of the student community irrespective of the countries they reside.
Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization

<h2>Abstract</h2> Changes in educational systems and English teaching strategies have increased the need for automatic methods for English Teaching Quality Evaluation (ETQE). A practical model for ETQE applies in different fields, determines the most relevant factors in teaching quality (TQ), and has optimal performance in different conditions. This paper presents a new method based on Artificial Intelligence (AI) and meta-heuristic algorithms to solve the ETQE problem. The proposed method performs the prediction process in two phases: "determination of related indicators" and "quality prediction". During the first phase, after introducing a set of 24 candidate indicators, an optimal subset of them having maximum correlation with ETQE and minimum redundancy are selected using Artificial Bee Colony (ABC) algorithm. In the second phase of the proposed method, a Classification and Regression Tree (CART) model optimized by ABC are applied to predict ETQ based on the indicators determined in the first phase. In this learning model, split points of decision nodes are determined by ABC in a way that the prediction accuracy would be maximized. The performance of the proposed method has been evaluated in two different teaching environments. The performance of the proposed method has been evaluated in two different teaching environments. The studied teaching environments are face-to-face (FF) and online classes that were held for middle school and university students, respectively. Based on the obtained results, the proposed method can predict the ETQ with an accuracy of more than 98.99% in both tested scenarios, which results in an increase of at least 1.11% compared to the previous methods. The efficiency of the proposed model in both studied scenarios prove the generality of this method to be used in real-world applications.
The limit joint distributions of statistics of three tests of the NIST package

Abstract For sequences of independent random variables having a Bernoulli distribution the joint distribution and the limit joint distribution of statistics of three tests of the NIST statistical package («Monobit Test», «Frequency Test within a Block», and «Cumulative Sums Test») are obtained. In the case when two blocks are used in «Frequency Test within a Block», pairwise covariance of these statistics is given.
Design of Mobile Phone Back Clip for Field Emergency Communication Based on Beidou Short Message

Aiming at the problems of poor communication reliability and poor emergency ability of communication terminals such as walkie-talkies and satellite phones, which are mainly used at present, due to limited signal coverage in the field, a field rescue terminal with reliable communication and strong emergency ability is developed. The main work of this paper is to develop the field emergency communication back clip, transmit the multimedia data in the mobile phone to the back clip through the self-made program, then send it out through the Beidou short message system, and transmit it to Tianjin Central Station by GEO satellite in space segment. After receiving and processing the signal, the multimedia information is obtained and then transmitted to the receiver, i.e. the rescue party, to greatly improve the success rate of rescue. It has important application value.
Knowledge Map Construction of Multi-source Heterogeneous Power Grid Data Fusion in the Power Internet of Things Environment

In order to realize the dynamic monitoring of multi-source heterogeneous power grid data in the power Internet of Things environment, it is necessary to construct the knowledge map. A knowledge map construction method of multi-source heterogeneous power grid data fusion in the power Internet of Things environment based on the combination and correction of re labeling method and projection method is proposed, and the knowledge clustering map features of multi-source heterogeneous power grid data generated by the basic clustering algorithm are used. The phase space reconstruction of multi-source heterogeneous power grid data is realized by projection method, and the subspace noise reduction and feature clustering model of multi-source heterogeneous power grid data in the power Internet of Things environment is established by combining the method of identification of grid connected installed capacity parameters. According to the probability distributed reorganization of the set of discrete variables of multi-source heterogeneous power grid data, the output status of multi-source heterogeneous power grid data at different times is calculated. According to the fusion method of state distribution difference, the dynamic correction and adaptive feedback adjustment of power grid data are realized by using the combination and correction method of re marking method and projection method to improve the accuracy of heterogeneous reconstruction of the knowledge map. The simulation test results show that this method can be used to construct the knowledge map of multi-source heterogeneous power grid data fusion, which has a good ability to express the internal structure information parameters of power grid data, a strong noise reduction performance for redundant data, and improves the dynamic monitoring and evaluation ability of power grid data.
Edge Computing Based Traffic Identification in Elastic Optical Networks for Smart Power Grid

The service awareness is of great importance and should be emphasized in Elastic Optical Networks (EON). Aimed to solve this problem, this article proposes an edge computing echo-state-network (E-ESN) model, which is improved greatly from original echo-state-network model by simplifying the reservoir computation. Moreover, the service traffic identification algorithm is also presented to match the multi-service demand. Thus, this proposed E-ESN based service awareness algorithm of software defined optical network is designed, together with the implement approach. Simulation results show that this proposed enhanced scheme can suite for the elastic optical network with better multi-service support ability.
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation

Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from training data deficiency. To this end, in our paper, we propose an end-to-end TIMT model fully making use of the knowledge from existing OCR and MT datasets to pursue both an effective and efficient framework. More specifically, we build a novel modal adapter effectively bridging the OCR encoder and MT decoder. End-to-end TIMT loss and cross-modal contrastive loss are utilized jointly to align the feature distribution of the OCR and MT tasks. Extensive experiments show that the proposed method outperforms the existing two-stage cascade models and one-stage end-to-end models with a lighter and faster architecture. Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models. (Our codes are available at: https://github.com/EriCongMa/E2TIMT )
Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation

Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on downstream tasks. Its difficulty lies in the large variation of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable and generalizable approach to identify text reading order with a multi-modal, multi-task graph convolutional network (GCN) running on a sparse layout based graph. Predictions from the model provide hints of bidimensional relations among text lines and layout region structures, upon which a post-processing cluster-and-sort algorithm generates an ordered sequence of all the text lines. The model is language-agnostic and runs effectively across multi-language datasets that contain various types of images taken in uncontrolled conditions, and it is small enough to be deployed on virtually any platform including mobile devices.
Evaluating Adversarial Robustness on Document Image Classification

Adversarial attacks and defenses have gained increasing interest on computer vision systems in recent years, but as of today, most investigations are limited to natural images. However, many artificial intelligence models actually handle documentary data, which is very different from real world images. Hence, in this work, we try to apply the adversarial attack philosophy on documentary data and to protect models against such attacks. Our methodology is to implement untargeted gradient-based, transfer-based and score-based attacks and evaluate the impact of defenses such as adversarial training, JPEG input compression and grey-scale input transformation on the robustness of ResNet50 and EfficientNetB0 model architectures. To the best of our knowledge, no such work has been conducted by the community in order to study the impact of these attacks on the document image classification task.
TDAE: Text Detection with Affinity Areas and Evolution Strategies

Text detection in natural scenes has evolved considerably in recent years. Segmentation-based methods are widely used for text detection because they are robust to detect text of any shape. However, most previous works focus on word-level detection and neglect the regions between adjacent words, which are helpful when some text instances are very close. In this paper, we propose a novel image feature named affinity area that exploits the area between two adjacent text instances to enhance the detection capability. We design an affinity module to generate annotations based on existing word-level annotations since no open dataset supports that. By optimizing this module, our segmentation-based network TDAE can predict text regions and affinity regions through which we can obtain the final detection results. Inspired by the evolutionary strategy (ES), our network also utilizes an additional novel fine-tuning step to update the parameters by adding adaptive but random perturbations, which is quite different from the traditional gradient descent approach. Competitive results on ICDAR (2013, 2015, 2017), CTW-1500, and SynthText benchmarks further demonstrate the effectiveness of TDAE.
Towards Making Flowchart Images Machine Interpretable

Computer programming textbooks and software documentations often contain flowcharts to illustrate the flow of an algorithm or procedure. Modern OCR engines often tag these flowcharts as graphics and ignore them in further processing. In this paper, we work towards making flowchart images machine-interpretable by converting them to executable Python codes. To this end, inspired by the recent success in natural language to code generation literature, we present a novel transformer-based framework, namely FloCo-T5. Our model is well-suited for this task, as it can effectively learn semantics, structure, and patterns of programming languages, which it leverages to generate syntactically correct code. We also used a task-specific pre-training objective to pre-train FloCo-T5 using a large number of logic-preserving augmented code samples. Further, to perform a rigorous study of this problem, we introduce the FloCo dataset that contains 11,884 flowchart images and their corresponding Python codes. Our experiments show promising results, and FloCo-T5 clearly outperforms related competitive baselines on code generation metrics. We make our dataset and implementation publicly available ( https://vl2g.github.io/projects/floco ).
Generalization of Fine Granular Extractions from Charts

Current approaches for high precision fine-grained visual extractions from charts are highly data intensive requiring thousands of annotated samples. Annotating a dataset and retraining for every new chart type with a shift in the spatial composition of chart elements, text role regions, legend preview styles, chart element shapes and text-role definitions, is a time-consuming and costly affair. Current approaches struggle to generalize to new chart types with a distributional shift across the above dimensions. In this paper, we define a novel attention and dynamic filtering based approach for extracting chart elements and identifying text-role regions, achieving SOTA results for visual extraction on charts for the PlotQA dataset, surpassing existing approaches by an mAP of 2.81% @0.90 IOU. More importantly, the methods proposed are designed to adapt to unseen chart types having the above mentioned shifts in chart element distributions. We demonstrate the generalization capabilities of our models trained on the PlotQA train set by providing chart extraction results on out-of-distribution charts selected from the LeafQA dataset. We achieve an mAP of 90.64% and 92.18% for @0.90 IOU for this well-curated out-of-distribution chart data in zero and few-shot settings, respectively.
ICDAR 2023 Competition on Indic Handwriting Text Recognition

This paper presents the competition report on Indic Handwriting Text Recognition (IHTR) held at the 17th International Conference on Document Analysis and Recognition (ICDAR 2023 IHTR). Handwriting text recognition is an essential component for analyzing handwritten documents. Several good recognizers are available for English handwriting text in the literature. In the case of Indic languages, limited work is available due to several challenging factors. (i) Two or more characters are often combined to form conjunct characters, (ii) most Indic scripts have around 100 unique Unicode characters, (iii) diversity in handwriting styles, (iv) varying ink density around the words, (v) challenging layouts with overlap between words and natural unstructured writing, and (vi) datasets with only a limited number of writers and examples. With this competition, we motivate the researchers to continue researching Indic handwriting text recognition tasks to prevent the risk of vanishing Indic scripts/languages. In this competition, we use a training set of an existing benchmark dataset [3, 4, 6]. We create a new manually annotated validation set and test set for validation and testing purposes. A total of eighteen different teams around the world registered for this competition. Among them, only six teams submitted the results along with algorithm details. The winning team Upstage KR achieves an average 95.94% Character Recognition Rate (CRR) and 88.31% Word Recognition Rate (WRR) over ten languages.
Scene Table Structure Recognition with Segmentation and Key Point Collaboration

This paper proposes a Segmentation and Key point Collaboration Network (SKCN) for structure recognition of complex tables with geometric deformations. First, we combine the cell regions of the segmentation branch and the corner locations of the key point regression branch in the SKCN to obtain more reliable detection bounding box candidates. Then, we propose a Centroid Filtering-based Non-Maximum Suppression algorithm (CF-NMS) to deal with the problem of overlapping detected bounding boxes. After obtaining the bounding boxes of all cells, we propose a post-processing method to predict the logical relationships of cells to finally recover the structure of the table. In addition, we design a module for online generation of tabular data by applying color, shading and geometric transformation to enrich the sample diversity of the existing natural scene table datasets. Experimental results show that our method achieves state-of-the-art performance on two public benchmarks, TAL_OCR_TABLE and WTW.
Ambigram Generation by a Diffusion Model

Ambigrams are graphical letter designs that can be read not only from the original direction but also from a rotated direction (especially with 180 $$^\circ $$ ). Designing ambigrams is difficult even for human experts because keeping their dual readability from both directions is often difficult. This paper proposes an ambigram generation model. As its generation module, we use a diffusion model, which has recently been used to generate high-quality photographic images. By specifying a pair of letter classes, such as ‘A’ and ‘B,’ the proposed model generates various ambigram images which can be read as ‘A’ from the original direction and ‘B’ from a direction rotated 180 $$^\circ $$ . Quantitative and qualitative analyses of experimental results show that the proposed model can generate high-quality and diverse ambigrams. In addition, we define ambigramability, an objective measure of how easy it is to generate ambigrams for each letter pair. For example, the pair of ‘A’ and ‘V’ shows a high ambigramability (that is, it is easy to generate their ambigrams), and the pair of ‘D’ and ‘K’ shows a lower ambigramability. The ambigramability gives various hints of the ambigram generation not only for computers but also for human experts. The code can be found at https://github.com/univ-esuty/ambifusion .
Towards Writer Retrieval for Historical Datasets

This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is trained on $$32\times 32$$ patches at keypoint locations. Additionally, we suggest a graph-based reranking algorithm called SGR to exploit similarities of the page embeddings to boost the retrieval performance. Our approach is evaluated on two historical datasets (Historical-WI and HisIR19). We include an evaluation of different backbones and NetRVLAD. It competes with related work on historical datasets without using explicit encodings. We set a new State-of-the-art on both datasets by applying our reranking scheme and show that our approach achieves comparable performance on a modern dataset as well.
You Only Look for a Symbol Once: An Object Detector for Symbols and Regions in Documents

We present YOLSO, a single stage object detector specialised for the detection of fixed size, non-uniform (e.g. hand-drawn or stamped) symbols in maps and other historical documents. Like YOLO, a single convolutional neural network predicts class probabilities and bounding boxes over a grid that exploits context surrounding an object of interest. However, our specialised approach differs from YOLO in several ways. We can assume symbols of a fixed scale and so need only predict bounding box centres, not dimensions. We can design the grid size and receptive field of a grid cell to be appropriate to the known scale of the symbols. Since maps have no meaningful boundary, we use a fully convolutional architecture applicable to any resolution and avoid introducing unwanted boundary dependency by using no padding. We extend the method to also perform coarse segmentation of regions indicated by symbols using the same single architecture. We evaluate our approach on the task of detecting symbols denoting free-standing trees and wooded regions in first edition Ordnance Survey maps and make the corresponding dataset as well as our implementation publicly available.
Receipt Dataset for Document Forgery Detection

The widespread use of unsecured digital documents by companies and administrations as supporting documents makes them vulnerable to forgeries. Moreover, image editing software and the capabilities they offer complicate the tasks of digital image forensics. Nevertheless, research in this field struggles with the lack of publicly available realistic data. In this paper, we propose a new receipt forgery detection dataset containing 988 scanned images of receipts and their transcriptions, originating from the scanned receipts OCR and information extraction (SROIE) dataset. 163 images and their transcriptions have undergone realistic fraudulent modifications and have been annotated. We describe in detail the dataset, the forgeries and their annotations and provide several baselines (image and text-based) on the fraud detection task.