Reviewerly/exHarmony huggingface dataset:
Robustly Recognizing Irregular Scene Text by Rectifying Principle Irregularities

Reading irregular scene text is a challenging problem in scene text recognition. Rectification is a popular measure to reduce irregularities of text in images. Existing rectification methods seek to rectify text images into a strictly regular form via free parametric transformation functions. However, they always suffer from information loss or severe deformation due to their poor constraints to the transformation functions. In our investigation, we found that CNN and attention are robust to many slight irregularities. What inspires us to propose a novel and effective rectification method that mainly rectifies the principle regularities, and leaves the slight irregularities to the CNN-LSTM-attention recognizer. Our rectification method first estimates the character densities and directions of the input image in a down-sampled map then finds a best fitting curve from a small predefined Bézier curve set, and finally rectifies the input image with a transformation function corresponding to the selected curve. Transformation functions are carefully designed so that they neither lose important visual information nor cause severe deformation. Extensive experiments on seven benchmark datasets show that our method achieves the state of the art performance in most cases, especially in curved text recognition.
A transformation of Bayesian statistics:Computation, prediction, and rationality

Bayesian approaches have long been a small minority group in scientific practice, but quickly acquired a high level of popularity since the 1990s. This paper shall describe and analyze this turn. I argue that the success of Bayesian approaches hinges on computational methods that make a class of models predictive that would otherwise lack practical relevance. Philosophically, however, this orientation toward prediction comes at a price. The new computational approaches change Bayesian rationality in an important way. Namely, they undercut the interpretation of priors, turning them from an expression of beliefs held prior to new evidence into an adjustable parameter that can be manipulated flexibly by computational machinery. Thus, in the case of Bayes, one can see a coevolution of computing technology, an exploratory-iterative mode of prediction, and the conception of rationality.
The Digital Teacher

The requirements of Education 4.0 and the unprecedented impacts of COVID-19 on education have been digitally transformative in learning and teaching processes. It has become essential to create the digital teacher with the required qualifications, rather than an optional endeavor, as improving teachers' technological skills has become even more important. To qualify the digital teacher, both pre- and in-service teachers should be trained from the perspectives of technology, pedagogy, and content, which are synthesized in the technological pedagogical content knowledge (TPACK) framework. As a technology-integrated approach to teaching, TPACK conceptualizes the three main knowledge domains a teacher should have, including the intersections of these domains. Thus, this chapter explores and analyzes TPACK studies on teacher training. In this chapter, the theoretical background of the TPACK framework is introduced, and research surrounding TPACK studies is reviewed for teacher training purposes. Finally, the results are discussed with key considerations along with future research directions.
Author verification of <i>Nahj Al-Balagha</i>

Abstract The primary purpose of this article is author verification of the Nahj Al-Balagha, a book attributed to Imam Ali and over which Sunni and Shi’i Muslims are proposing different theories. Given the morphologically complex nature of Arabic, we test whether morphological segmentation, applied to the book and works by the two authors suspected by Sunnis to have authored the texts, can be used for author verification of the Nahj Al-Balagha. Our findings indicate that morphological segmentation may lead to slightly better results than whole words and that regardless of the feature sets, the three sub-corpora cluster into three distinct groups using principal component analysis, hierarchical clustering, multi-dimensional scaling, and bootstrap consensus trees. Supervised classification methods such as Naive Bayes, Support Vector Machines, k Nearest Neighbours, Random Forests, AdaBoost, Bagging, and Decision Trees confirm the same results, which is a clear indication that (1) the book is internally consistent and can thus be attributed to a single person and (2) it was not authored by either of the suspected authors.
Detection Method of Limb Movement in Competitive Sports Training Based on Deep Learning

Traditional methods have the problems of insufficient accuracy and slow speed in human posture detection. In order to solve the above problems, a limb movement detection method in competitive sports training based on deep learning is proposed. The force change parameters of sports limb movements in the process of sports are computed to achieve the detection of limb movements in competitive sports training, and the limb movement characteristics in competitive sports training are extracted using a deep learning algorithm. The experimental results show that the limb movement detection method based on deep learning in competitive sports training has significantly higher detection accuracy and faster speed.
Learning Collaborative Action Plans from YouTube Videos

Videos from the World Wide Web provide a rich source of information that robots could use to acquire knowledge about manipulation tasks. Previous work has focused on generating action sequences from unconstrained videos for a single robot performing manipulation tasks by itself. However, robots operating in the same physical space with people need to not only perform actions autonomously, but also coordinate seamlessly with their human counterparts. This often requires representing and executing collaborative manipulation actions, such as handing over a tool or holding an object for the other agent. We present a system for knowledge acquisition of collaborative manipulation action plans that outputs commands to the robot in the form of visual sentence. We show the performance of the system in 12 unlabeled action clips taken from collaborative cooking videos on YouTube. We view this as the first step towards extracting collaborative manipulation action sequences from unconstrained, unlabeled online videos.
Multi-hop interactive attention based classification network for expert recommendation

Community question answering (CQA) is a popular platform where users can ask questions or solve the questions proposed by other users. The expert recommendation aims at providing high-quality answers for the newly proposed questions in time, which is the key to a successful CQA. Questions in CQA usually consist of two parts, a subject which describes the main point, and a body which gives the details of the question. In previous studies, researchers usually ignore the differences between the subject and the body and concatenate them as a whole. In this paper, we propose a multi-hop interactive attention based classification network (MIACN) to recommend experts for newly proposed questions. In our model, the subject and the body are seen as two separate parts. A multi-hop attention is used to capture the multiple latent interactions among the two parts. Then, a high-level representation of the question is generated from the interactions. Experiment results on two real-world datasets demonstrate the effectiveness of our model.
Parameter Optimization of Spark in Heterogeneous Environment Based on Hyperband

There are many default parameters in Spark, which have a great influence on job performance. Different jobs require different parameter combos, resulting in the need to tune parameters with experience which causes low efficiency. To improve the performance of Spark in the heterogeneous environment, the Hyperband algorithm is used to optimize the Spark parameters to improve the efficiency of the Spark platform. To emulate the actual production environment and improve operation efficiency as possible, this study uses Spark on yarn mode to test. Experiments are carried out on WordCount and Sort job types. The results show that, taking 5GB dataset as an example, compared with the default parameters., this method reduces the job execution time by 12.94&#x0025; and 10.41&#x0025;, respectively.
Pinned loads: taming speculative loads in secure processors

In security frameworks for speculative execution, an instruction is said to reach its Visibility Point (VP) when it is no longer vulnerable to pipeline squashes. Before a potentially leaky instruction reaches its VP, it has to stall—unless a defense scheme such as invisible speculation provides protection. Unfortunately, either stalling or protecting the execution of pre-VP instructions typically has a performance cost.
Task Adaptive Network for Image Restoration with Combined Degradation Factors

Existing methods have achieved excellent performance on image restoration, but most of them are designed for one type of degradation. However, the weather is complex in the real world. So networks designed for single tasks are usually difficult to apply. Therefore, we propose a task-adaptive attention module to enable the network to restore images with multiple degradation factors. The task-adaptive attention module mainly includes three parts: Task-Adaptive sub-network, Task Channel Attention, and Task Operation Attention. To evaluate the model, we construct a mixed degradation factors dataset that combines three degradation factors of rain, haze, and raindrop. The experimental results show that our method not only better restores images with mixed degradation factors, but also show competitive results compared to the state-of-the-art models of each task.
Feature Extraction Methods for CT-Scan Images Using Image Processing

Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. Once the CT scan image is captured, Doctors diagnose it to detect abnormal or normal condition of the captured of the patient’s body. In the computerized image processing diagnosis, CT-scan image goes through sophisticated phases viz., acquisition, image enhancement, extraction of important features, Region of Interest (ROI) identification, result interpretation etc. Out of these phases, a feature extraction phase plays a vital role during automated/computerized image processing to detect ROI from CT-scan image. This phase performs scientific, mathematical and statistical operations/algorithms to identify features/characteristics from the CT-scan image to shrink image portion for diagnosis. In this chapter, I have presented an extensive review on “Feature Extraction” step of digital image processing based on CT-scan image of human being.
Single-shot Path Integrated Panoptic Segmentation

Panoptic segmentation, which is a novel task of unifying instance segmentation and semantic segmentation, has attracted a lot of attention lately. However, most of the previous methods are composed of multiple pathways with each pathway specialized to a designated segmentation task. In this paper, we propose to resolve panoptic segmentation in single-shot by integrating the execution flows. With the integrated pathway, a unified feature map called Panoptic-Feature is generated, which includes the information of both things and stuffs. Panoptic-Feature becomes more sophisticated by auxiliary problems that guide to cluster pixels that belong to the same instance and differentiate between objects of different classes. A collection of convolutional filters, where each filter represents either a thing or stuff, is applied to Panoptic-Feature at once, materializing the single-shot panoptic segmentation. Taking the advantages of both top-down and bottom-up approaches, our method, named SPINet, enjoys high efficiency and accuracy on major panoptic segmentation benchmarks: COCO and Cityscapes.
Application of Mobile Technology in College English Vocabulary Teaching

In order to improve the quality of college English vocabulary teaching, this paper studies the application of mobile technology in college English vocabulary teaching. The advantages of mobile technology are analyzed, and different mobile technology communication deployment schemes are determined according to different needs and environments of English vocabulary learning, so as to build a college English vocabulary teaching system, which is mainly composed of the user layer, mobile network layer, and English vocabulary data layer. Among them, the main functions of the user layer are user login, system information setting, English vocabulary resource browsing, uploading English vocabulary homework, etc., and determine user preference data through user logs. The mobile network layer is designed using mobile technology to create the delay control technique of the system mobile communication, and the system mobile network layer’s LAN is set up to increase data transmission speed. Finally, set up the database according to the English vocabulary of English words field, calculating Euclidean distance to determine the relationship between them, and then through random set classification vocabulary data types, calculate the similarity degree of the data extraction vocabulary data characteristics and remove high similarity of the English vocabulary data to reduce the difficulty of system storage. The experimental findings suggest that the mobile technology-based system has high performance and dependability and may be widely used in practice.
Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Stock tape reading involves surveilling stock prices once in a while and recording stock prices. The method of observing stock prices may be television or stock exchange. The time step for recoding stock prices is every stock user’s experience and their theory, perhaps 3 min or 2 h and so on. As an example, the Taiwan stock market starts at 9:00 a.m. to 13:30 p.m. It will have a 4 h operating time. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, but finally, it is the stock user’s experience. Therefore, the meaning of tape reading is to record the stock price, but its concept should have no prediction purpose. This study used thread technology and proposed a tape-reading method with web scraping. This method can periodically scrape stock prices and generate a stock price sequence to Excel file. This application can satisfy the demand of these stock users, who are called day trading users. Because these day trading users want to gain stock price sequences minute by minute, rather than the stock exchange format day by day, and also ones which are better than the those provided by the stock website service, because its stock sequence format is limited and not normalized, these day trading users think that minute-by-minute stock price sequences are very clear to forecast. This study implemented the prior scheme and designed the GUI to query a company’s stock price and its stock news, even per second, etc., and how long it took to update the stock price, and the GUI also included a time-up feature to stop scraping stock prices if users just wanted to scrape stock prices periodically.
Automatic Brightness Control for Face Analysis in Near-Infrared Spectrum

Face analysis is a broad and well-established research area whose main focus is put on face detection, segmentation, recognition and facial features extraction. A crucial prerequisite to face analysis algorithms properly work is to have an input image of high quality with similar properties in different conditions. For this reason, near-infrared images are used due to being more robust to change in lighting conditions and time of day than the visible light spectrum images. Automatic brightness control is used to properly adjust scene brightness to extract useful information. A novel algorithm implementation for the automatic brightness control is proposed based on a split range feedback controller with a camera occlusion detection included. The proposed algorithm is accurate, fast and suitable for real-time embedded system implementation.
Improved Shape Parameter Estimation for the Three-Parameter Log-Logistic Distribution

The log-logistic distribution is widely used in different fields of study such as survival analysis, hydrology, insurance, and economics. Recently, Ahsanullah and Alzaatreh studied the best linear unbiased estimators for the location and the scale parameters of the three-parameter log-logistic model. The same authors also propose a shift-invariant Hill estimator for the unknown shape parameter. In this work, we propose a new estimation method for the shape parameter. We derive its nondegenerate asymptotic behaviour and analyse its finite sample performance through a Monte Carlo simulation study. To have precise estimates, we present a method for selecting the threshold. To illustrate the improvement achieved, efficiency comparisons are also provided.
Graph Minimally-supervised Learning

Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning.
Boolean-based Two-in-One Secret Image Sharing by Adaptive Pixel Grouping

The two-in-one secret image sharing (TiOSIS) technique is a hybrid scheme that protects a secret image by combining visual cryptography (VCS) and polynomial-based secret image sharing (PSIS). There are two decoding methods available in TiOSIS: stacking-to-see decryption and lossless image recovery. However, the majority of current TiOSIS methods use Lagrange interpolation to precisely reconstruct the secret, which would result in intense computations. In this article, an efficient TiOSIS scheme using Boolean XOR operation for lossless image recovery is proposed. The proposed scheme consists of three building blocks: shared data generation, shadow construction, and image decryption. In shared data generation, the grayscale secret image is processed by a Boolean-based SIS to derive the shared bits. In shadow construction, an adaptive pixel grouping (APG) strategy is utilized to determine a grouping pattern. The halftone image adjustment algorithm is adopted to generate a suitable halftone image. With the grouping pattern and halftone image, we construct the shadows via the group-pixel embedding and sharing approach. In image decryption, we can reveal the secret image by stacking-to-see decoding or Boolean-based lossless image recovery. Extensive experiments and comparisons are illustrated to show the effectiveness and benefits of the proposed scheme.
Multiple Object Tracking and Forecasting: Jointly Predicting Current and Future Object Locations

This paper introduces a joint learning architecture (JLA) for multiple object tracking (MOT) and multiple object forecasting (MOF) in which the goal is to predict tracked objects’ current and future locations simultaneously. MOF is a recent formulation of trajectory forecasting where the full object bounding boxes are predicted rather than trajectories alone. Existing works separate multiple object tracking and multiple object forecasting. Such an approach can propagate errors in tracking to forecasting. We propose a joint learning architecture for multiple object tracking and forecasting (MOTF). Our approach reduces the chances of propagating tracking errors to the forecasting module. In addition, we show, through a new data association step, that forecasting predictions can be used for tracking objects during occlusion. We adapt an existing MOT method to simultaneously predict current and future object locations and confirm that JLA benefits both the MOT and MOF tasks.
A fast supervised density-based discretization algorithm for classification tasks in the medical domain

Discretization is a preprocessing technique used for converting continuous features into categorical. This step is essential for processing algorithms that cannot handle continuous data as input. In addition, in the big data era, it is important for a discretizer to be able to efficiently discretize data. In this paper, a new supervised density-based discretization (DBAD) algorithm is proposed, which satisfies these requirements. For the evaluation of the algorithm, 11 datasets that cover a wide range of datasets in the medical domain were used. The proposed algorithm was tested against three state-of-the art discretizers using three classifiers with different characteristics. A parallel version of the algorithm was evaluated using two synthetic big datasets. In the majority of the performed tests, the algorithm was found performing statistically similar or better than the other three discretization algorithms it was compared to. Additionally, the algorithm was faster than the other discretizers in all of the performed tests. Finally, the parallel version of DBAD shows almost linear speedup for a Message Passing Interface (MPI) implementation (9.64× for 10 nodes), while a hybrid MPI/OpenMP implementation improves execution time by 35.3× for 10 nodes and 6 threads per node.