Reviewerly/exHarmony huggingface dataset:
Document Image Binarization using Image Segmentation Technique

Segmentation of text from badly degraded document images is an extremely difficult assignment because of the high inter/Intra variety between the record foundation and the frontal area text of various report pictures. Picture preparing and design acknowledgment algorithms set aside more effort for execution on a solitary center processor. Designs Preparing Unit (GPU) is more mainstream these days because of its speed, programmability, minimal expense and more inbuilt execution centers in it. The primary objective of this exploration work is to make binarization quicker for acknowledgment of a huge number of corrupted report pictures on GPU. In this framework, we give another picture division calculation that every pixel in the picture has its own limit proposed. We are accomplishing equal work on a window of m*n size and separate article pixel of text stroke of that window. The archive text is additionally sectioned by a nearby edge that is assessed dependent on the forces of identified content stroke edge pixels inside a nearby window.
Comprehensive Studies for Arbitrary-shape Scene Text Detection

Numerous scene text detection methods have been proposed in recent years. Most of them declare they have achieved state-of-the-art performances. However, the performance comparison is unfair, due to lots of inconsistent settings (e.g., training data, backbone network, multi-scale feature fusion, evaluation protocols, etc.). These various settings would dissemble the pros and cons of the proposed core techniques. In this paper, we carefully examine and analyze the inconsistent settings, and propose a unified framework for the bottom-up based scene text detection methods. Under the unified framework, we ensure the consistent settings for non-core modules, and mainly investigate the representations of describing arbitrary-shape scene texts, e.g., regressing points on text contours, clustering pixels with predicted auxiliary information, grouping connected components with learned linkages, etc. With the comprehensive investigations and elaborate analyses, it not only cleans up the obstacle of understanding the performance differences between existing methods but also reveals the advantages and disadvantages of previous models under fair comparisons.
Testing Dynamic Environments: Back to Basics.

We continue the line of work initiated by Goldreich and Ron (Journal of the ACM, 2017) on testing dynamic environments and propose to pursue a systematic study of the complexity of testing basic dynamic environments and local rules. As a first step, in this work we focus on dynamic environments that correspond to elementary cellular automata that evolve according to threshold rules. Our main result is the identification of a set of conditions on local rules, and a meta-algorithm that tests evolution according to local rules that satisfy the conditions. The meta-algorithm has query complexity poly$ (1/\epsilon) $, is non-adaptive and has one-sided error. We show that all the threshold rules satisfy the set of conditions, and therefore are poly$ (1/\epsilon) $-testable. We believe that this is a rich area of research and suggest a variety of open problems and natural research directions that may extend and expand our results.
LibGuides: Lee Connected Collaboration: Destiny 101

Making the library catalog your go to source for instructional material support, research tools, and high interest reading materials.
Human Pose Regression with Residual Log-likelihood Estimation

Heatmap-based methods dominate in the field of human pose estimation by modelling the output distribution through likelihood heatmaps. In contrast, regression-based methods are more efficient but suffer from inferior performance. In this work, we explore maximum likelihood estimation (MLE) to develop an efficient and effective regression-based methods. From the perspective of MLE, adopting different regression losses is making different assumptions about the output density function. A density function closer to the true distribution leads to a better regression performance. In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution. Concretely, RLE learns the change of the distribution instead of the unreferenced underlying distribution to facilitate the training process. With the proposed reparameterization design, our method is compatible with off-the-shelf flow models. The proposed method is effective, efficient and flexible. We show its potential in various human pose estimation tasks with comprehensive experiments. Compared to the conventional regression paradigm, regression with RLE bring 12.4 mAP improvement on MSCOCO without any test-time overhead. Moreover, for the first time, especially on multi-person pose estimation, our regression method is superior to the heatmap-based methods. Our code is available at this https URL
Improve the Ability to Learn to Read Early by Using SAS Methods

&lt;p&gt;&lt;em&gt;Bahasa Indonesia is one of the fields of study taught in elementary schools, in the framework of coaching and passion and development of the age of elementary school children, the skills of Bahasa Indonesia are often an obstacle for students in terms of reading and writing. The purpose of the study, knowing the improvement of early reading skills using the Synthetic Analytics Structural (SAS) method. This type of research is Class Action Research using cycle models. The researchers showed that in the initial condition, the average reading skills of students were 67.71 with a classical completion rate of 42.85%. In cycle I, the average reading skills of students was 68.42 with a classical completion rate of 57.14%. In cycle II, the average student score was 76.71 with a classical completion rate of 85.71%. From the whole cycle that has been done, it can be concluded that the teacher has been able to improve the reading skills of the beginning of Grade I students by using the method of stuktural analytics sintatik. Each cycle always has a positive impact towards improving the development of early reading ability of Grade I students.&lt;/em&gt;&lt;/p&gt;
Folk Belief on Pancur Gading Site in Deli Tua Village, Sumatera Utara Province, Indonesia

Folk belief through magical practices is an integral part of the discussion about the Pancur Gading Site, located in Deli Tua Village, North Sumatra, Indonesia. Through this folk belief, people come with their own goals and purposes. The remains of this site can be seen by the rushing water of the two showers, namely ‘pancuran putri’ (princess shower) and ‘pancuran panglima’ (the commander's shower). People believe by using this water, all the problems in their life can be resolved immediately. Various magical practices are also held to fulfill human desires for the problems they face. The religious behavior of the people who believe in the properties of Pancur Gading is based on the folk belief in the worshiped figures. The legend of Putri Hijau provides a conception of revered supernatural figures, including the princess known as Putri Hijau or Nini Biring, her first brother (kakek naga), the second (kakek meriam), her commanders, and the ancestors who were worshiped according to cultural background of their respective communities. This belief is strengthened by the existence of media from supernatural experts who connect to patients. This paper is an effort to make an inventory of the various folk beliefs and traditions related to the Pancur Gading Site as an intangible cultural heritage.
AGE ESTIMATION FROM FACIAL IMAGES USING DEEP LEARNING

users’ identity. To estimate the real age of a person, a robust and reliable system should be developed that can consistently dealing with images under real conditions. Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many deep learning tasks such as face recognition and detection. However, the automatic estimation of a human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. Hand-crafted based model suffers from many limitations while extracting the vital features from images. Deep learning is a more advanced technique that depends on the hierarchical learning strategy. The basic representation is encoded in the lower layers, while the more abstract concepts are formed in the higher layers using the basic concepts received from the low layers. Thus, this way of learning gives us the power to replace the hand-crafted methods with deep learning.In this work, we proposed two models for age estimation. The first model is based on Wide Residual Network. As a primary step, we trained the model from scratch using unconstrained dataset for two parallel tasks: real age estimation and gender classification. Then the model was finetuned using constrained dataset for real age estimation and for age group classification using unconstrained dataset. The second model is based on VGGFace-VGG16. This model was pretrained on face recognition task using a large dataset. Thus, reusing this model can boost the age learning process. Two different approaches were proposed to finetune the pretrained VGGFace model for age estimation. We evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset, the model obtained an error of 3.446, and on the unconstrained UTKFace dataset, the achieved error by our model reaches to 4.867. We also fine-tuned the model for age groups classification task on the unconstrained Adience dataset, and our model achieved an accuracy of 62.4%.
Clinical Relation Extraction Using Transformer-based Models

The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performances on various NLP benchmarks. In the clinical domain, researchers also have investigated transformer models for clinical applications. The goal of this study is to systematically explore three widely used transformer-based models (i.e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain. We developed a series of clinical RE models based on three transformer architectures, namely BERT, RoBERTa, and XLNet. We evaluated these models using 2 publicly available datasets from 2018 MADE1.0 and 2018 n2c2 challenges. We compared two classification strategies (binary vs. multi-class classification) and investigated two approaches to generate candidate relations in different experimental settings. In this study, we compared three transformer-based (BERT, RoBERTa, and XLNet) models for relation extraction. We demonstrated that the RoBERTa-clinical RE model achieved the best performance on the 2018 MADE1.0 dataset with an F1-score of 0.8958. On the 2018 n2c2 dataset, the XLNet-clinical model achieved the best F1-score of 0.9610. Our results indicated that the binary classification strategy consistently outperformed the multi-class classification strategy for clinical relation extraction. Our methods and models are publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction. We believe this work will improve current practice on clinical relation extraction and other related NLP tasks in the biomedical domain.
Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines

Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human coders today. However, the biggest challenge is directly identifying appropriate medical codes out of several thousands of high-dimensional codes from unstructured free-text clinical notes. In the past three years, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LTSM) networks, there have been vast improvements in tackling the most challenging benchmark of the MIMIC-III-full-label inpatient clinical notes dataset. This progress raises the fundamental question of how far automated machine learning (ML) systems are from human coders' working performance. We assessed the baseline of human coders' performance on the same subsampled testing set. We also present our Read, Attend, and Code (RAC) model for learning the medical code assignment mappings. By connecting convolved embeddings with self-attention and code-title guided attention modules, combined with sentence permutation-based data augmentations and stochastic weight averaging training, RAC establishes a new state of the art (SOTA), considerably outperforming the current best Macro-F1 by 18.7%, and reaches past the human-level coding baseline. This new milestone marks a meaningful step toward fully autonomous medical coding (AMC) in machines reaching parity with human coders' performance in medical code prediction.
Diseño instruccional geométricos en entorno real y virtual

In geometric instructional design, in virtual and real environments, it formulates a new way of constructing learning, with the combination of real objective resources (considering positivism) and analog (technological). Resources that are reversed as sets of relating elements, which from their combination create a new way of observing subjective reality from the induced objectivity. This design has the structure of the where?, the how ?, the when and the with what. These considerations allow to formulate the following question that serves as a guide that the structure of the research design will have: What is the contribution of instructional design in virtual and real environments to teach geometry? Based on this question, the environmental research was directed interpretive hermeneutic, and then from the analysis and synthesis to answer the question in a theoretical Dialogue and then make the conclusions and recommendations.
Improving Ability in Identifying Procedure Text Elements Using Visual Audio Media

The research aims to determine the process of increasing the identification of procedural text elements using audio-visual media in class VII SMP Negeri 8 OKU and the results of the improvement to identify elements of procedural text using audio-visual media in class VII students of SMP Negeri 8 OKU.The method used in this study is a classroom action research method.The research subjects were 32 students of class VII.C at SMP Negeri 8 OKU in the 2020/2021 academic year.Data collection was carried out by identifying the elements of the procedural text, observation, and documentation.The data analysis technique uses the percentage formula.The results of the study were the increase in student learning outcomes in each cycle starting from the initial cycle (pre-cycle) the average value of students had increased.The increase in the results of observations obtained by students has increased from the learning outcomes of the active category and increased to the active category.The increase in the level of student completeness starting from the pre-cycle of students who reached the Minimum Completion Criteria (KKM) had increased and students who obtained scores below the Minimum Completeness Criteria (KKM) had decreased.
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models

Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. With recent state-of-the-art attention-based automatic speech recognition (ASR) structure, NAR can realize promising real-time factor (RTF) improvement with only small degradation of accuracy compared to the autoregressive (AR) models. However, the recognition inference needs to wait for the completion of a full speech utterance, which limits their applications on low latency scenarios. To address this issue, we propose a novel end-to-end streaming NAR speech recognition system by combining blockwise-attention and connectionist temporal classification with mask-predict (Mask-CTC) NAR. During inference, the input audio is separated into small blocks and then processed in a blockwise streaming way. To address the insertion and deletion error at the edge of the output of each block, we apply an overlapping decoding strategy with a dynamic mapping trick that can produce more coherent sentences. Experimental results show that the proposed method improves online ASR recognition in low latency conditions compared to vanilla Mask-CTC. Moreover, it can achieve a much faster inference speed compared to the AR attention-based models. All of our codes will be publicly available at https://github.com/espnet/espnet.
PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution

Since the superiority of Transformer in learning long-term dependency, the sign language Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and Translation (SLT). However, there are several issues with the Transformer that prevent it from better sign language understanding. The first issue is that the self-attention mechanism learns sign video representation in a frame-wise manner, neglecting the temporal semantic structure of sign gestures. Secondly, the attention mechanism with absolute position encoding is direction and distance unaware, thus limiting its ability. To address these issues, we propose a new model architecture, namely PiSLTRc, with two distinctive characteristics: (i) content-aware and position-aware convolution layers. Specifically, we explicitly select relevant features using a novel content-aware neighborhood gathering method. Then we aggregate these features with position-informed temporal convolution layers, thus generating robust neighborhood-enhanced sign representation. (ii) injecting the relative position information to the attention mechanism in the encoder, decoder, and even encoder-decoder cross attention. Compared with the vanilla Transformer model, our model performs consistently better on three large-scale sign language benchmarks: PHOENIX-2014, PHOENIX-2014-T and CSL. Furthermore, extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on translation quality with $+1.6$ BLEU improvements.
Training Requirements among MDTCA Officers for Professional Training Improvement via Mobile Learning

M-Learning is a very suitable alternative for learning in improving professional training development. In order to implement M-Learning, it is necessary to do a preliminary study. There are three main aspects that need to be considered such as content, context, and characteristics of potential users. However, there is no specific study on the Ministry of Domestic Trade and Consumer Affairs (MDTCA) enforcement officers for the use of M-Learning for their training purposes. This case study involves eight experts and the data was collected using quantitative data and interview data. The instrument of this study consists of questionnaires and it was analyzed using frequency and percentage data. The reliability value for the questionnaire was 0.91 alpha Cronbach, besides the interview protocol method for expert validation. The expert was selected purposively based on their experience and expertise in online training. The interview data was analyzed using thematic methods. The findings of the study show that MDTCA enforcement officers need extra training for professional development. Besides that, its shows that training via M-Learning is needed continuously to improve the efficiency of enforcement officers. The implications of this study will improve the quality of training MDTCA Northern Zone Malaysia to enhance the competency of the enforcement officers.
Augmented Reality Navigation for College Campus

We all already knows and used every time the outdoor navigation system which are based on GPS technology, but this is not possible for indoor navigation so with the help of augmented reality technology, we develop indoor navigation application which can be deployed in large buildings like hospital, college campus, airport and guide visitors to reach the desired destination point. With the help of this application, user get guided instructions like when he have to moved left, right or go straight and reach the destination location without any trouble. There is the navigation of the path in our system from starting position to the destination where the user wants to reach his destination with navigation system assistance. The user selects the location where he/she wants to reach in the campus, scans his/her current location and scans the beginning position. The 3D augmented visual pointing arrows on the device attendant the user to reach the desired location or destination point. The whole system was developed using Unity and Android Studio along with some other platforms, and will be ready to use in Android operating system. This is new thing and an innovation for the local users of the Android and Unity. This project totally be used for simulations and user interactions as a developing platform.
Anomaly Detection Based on Multiple-Hypothesis Autoencoder

Recently Autoencoder(AE) based models are widely used in the field of anomaly detection. A model trained with normal data generates a larger restoration error for abnormal data. Whether or not abnormal data is determined by observing the restoration error. It takes a lot of cost and time to obtain abnormal data in the industrial field. Therefore the model trains only normal data and detects abnormal data in the inference phase. However, the restoration area for the input data of AE is limited in the latent space. To solve this problem, we propose Multiple-hypothesis Autoencoder(MH-AE) model composed of several decoders. MH-AE model increases the restoration area through contention between decoders. The proposed method shows that the anomaly detection performance is improved compared to the traditional AE for various input datasets.
Latent space visualization of half face and full face by generative model

Generally, most face detection and recognition tasks are based on the training of intact facial images and their corresponding labels. The training image is supposed to contain as much facial area as possible, and sometimes expanding the training image area to the upper body may also enhance the learning ability. However, we noticed that both the three-dimensional structure and two-dimensional appearance from the frontal view of human faces are bilaterally symmetrical. Few research makes use of this characteristics to simplify the learning process. We have proposed a flipping strategy to apply the facial symmetrical characteristic to transfer learning and proved training with half faces can also achieve equivalent performance in face recognition for a small group of individuals. This paper extend the transfer learning of cropped half face images for face recognition rather than flipping the half face. The facial symmetrical characteristics is utilized to improve face recognition through transfer learning of only a half of the common human face image. We also investigate and explain the reason why the half face area is enough to accurately classify small groups of individuals. A variational autoencoder network is utilized to impose the probability distribution on the facial latent space. Finally, the dimensions of the facial latent space are reduced to visualize the distributed perceptual manifold for face identity.