Reviewerly/exHarmony huggingface dataset:
Comparative analysis of the proprietary navigation system and the built-in Unity engine tool

Navigation systems are advanced tools that allow you to create characters intelligently moving around the game world. The purpose of this thesis was to conduct a comparative analysis of the proprietary navigation system “AlchemyNavigation” and the built-in tool of the Unity engine - “NavMesh”. An proprietary application created with the Unity engine was used to conduct the research, under which identical scenarios based on the tested systems were implemented. Finally, on the basis of the collected results and documentation of the research objects, a comparative analysis was carried out and proved the thesis that own solutions can match the default solutions.
한국어 주어일치소에 대한 새로운 접근: 음성형식부 일치 분석

This paper presents a PF-based analysis of the so-called subject agreement forms in Korean such as -si- and -tul. These morphemes exhibit the properties that are not consistent with the predictions of syntactic agreement; their realizations are optional, and their overall distributions cannot be fully captured by any syntactic rules. In addition, semantic interpretations are not substantially affected regardless of their presence or absence. Based on these facts, this paper argues that they should rather be taken as agreement forms at PF, thus inserted by the application of a node-sprouting rule at PF. Their status as subject agreement forms will also be refuted, instead generalizing them as agreeing with their C-commanding antecedents being in the same phase.
Applying the majority voting rule in acoustic detection and classification of drones

This paper discusses an approach to target detection and classification based on acoustic signals collected using one single microphone.This study has applications to sonar or any other sound event classification system.We divide the problem into two parts, namely feature extraction and target detection and classification.We use an optimization step based on human auditory uncertainty.We employ a majority voting rule for every set of feature vectors, i.e., an estimate is only performed if the majority agrees.We conducted experiments using a single channel of the AIRA-UAS dataset, a public database of raw drone noises collected with an array of microphones mounted on a drone.This dataset comprises many different kinematics, with different spectra.The features we used are based on the Mel-Frequency Cepstral Coefficients (MFCC) and the Short-Time Fourier Transform of raw signals.We used the K-Nearest Neighbors algorithm for classification and adopted the crossvalidation strategy to evaluate the method.We observed that the use of MFCC results in less biased estimations, which favors the voting strategy.The detection in the proposed method reached a probability of false positive near 0%, even with a small set of votes, and a classification accuracy of 99.1%.These metrics satisfy the requirements of most civilian and military applications.
Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text.

In this shared task, we seek the participating teams to investigate the factors influencing the quality of the code-mixed text generation systems. We synthetically generate code-mixed Hinglish sentences using two distinct approaches and employ human annotators to rate the generation quality. We propose two subtasks, quality rating prediction and annotators' disagreement prediction of the synthetic Hinglish dataset. The proposed subtasks will put forward the reasoning and explanation of the factors influencing the quality and human perception of the code-mixed text.
A novel pooling method for CNN model based on discrete cosine transform

Deep learning can be used to learn huge volume of data, which will be processed through hidden layers and according to the number of hidden layers ,filter size and numbers and the required computation cost is increased because of the size of raw data, this problem can be avoided by using pooling techniques, different method s are proposed to extract the basic features of the signal instead of all signal, but unfortunately this operation may introduce some noise or omission because of elimination important data from the signal. In this paper, A novel pooling method are proposed based on discrete cosine transform , this method is utilized DCT technique to reduce spatial redundancy of image by transform the spatial domain into frequency domain , which can preserve the most significant image information from the other coefficients, which represents the other details information of the image, so discard these less important coefficients. Its effect will be slight and this can reduce the eliminated information as compared with other methods. After applying DCT, we crop the most significant coefficients to be used in the reconstructed data by applying inverse DCT . then the result is combined in different methods with Max pooling and average pooling methods, this new structure can reduce the effect of discarding most important information and reduce the drawbacks of average and Max. pooling method. لإhe results are proved that our proposed methods are outperformed some standard methods and can be used in more application.
LSC-Tomball: Library Research Guides: Monologues: Find eBooks

READ, READ, READ! Whole books have been written on this subject but the best way to find a good monologue for you is to simply read as many plays as you can. (Most plays can be read in less than an hour). The key to a successful performance is finding a
Analyzing the Relationship among EFL College Students’ Learning Styles, Strategies, and Awareness of Learning in Online Classes During the Pandemic

The purpose of this study was to find out the relationships among learning styles, strategies, awareness of learning of Korean college students as they took an online class during the pandemic. In order to achieve the purpose, specific questions were established: 1) what types of learning style do Korean college students have? 2) what kinds of learning strategy do they use in online classes? and 3) how do their learning styles, strategies, and awareness of learning correlate with each other? Twenty-two college students responded to the questionnaires; the O’Brien (1989)’s Learning Styles Questionnaire, the Oxford (1990)’s Strategy Inventory of Language Learning, and the Rovai (2002)’s Classroom Community Scale. The Pearson correlation efficient was used for the relationships among the three variables. The results of data analysis showed that the least preferred learning style was the auditory style, and more than half of the students indicated that they would learn best as they were involved in some kinds of physical movement. Also, they used the meta-cognitive strategy the most, and the affective strategy the least. Finally, kinesthetic learners were likely to have a statistically significant correlation with the sub-construct, ‘Mutual dependence and cooperation.’ Based on the results, suggestions and implications for the classroom are presented.
An Hybrid CPU-GPU Parallel Multi-tracking Framework for Long-Term Video Sequences

The automatic evaluation of video content is today one of the biggest challenges in computer Vision. When the purpose is to work with static surveillance cameras, where most of the time the scenes do not change, a full Convolutional Network (CNN) approach seems to require too much CPU effort, specially when the objects are slightly moving between different frames. On the other side, visual tracking has seen great recent advances in either speed or accuracy but still remain scarce when have to deal with long videos where new objects constantly come into the scene and others disappear.In this paper, we present a parallelization scheme to handle multiple instances of object tracking. The main purpose is reduce overall processing time. The idea is to use already pre-trained CNNs for discovering objects and a parallel multi-tracker for following them, using both CPU and GPU devices.Our multi-tracker framework consists of three main components, a movement detector, an object classification and a tracker. The detector is used as an initialization for trackers. When there are plenty of objects in the scene, the other two components are incorporated for reducing CPU effort. The first one is a scheduler than prioritizes tracking those objects that seems more relevant than the others. The second one, is a GPU memory handler, that lets adapt the framework to different hardware configuration. We evaluate this framework in different cases and cameras configurations, reaching reasonable speed-up and confidence.
The Effect of Hemolysis on Hematology Laboratory Test

Hemolysis in blood samples is a problem that is commonly found in laboratories. This has a potential effect on the qualiy of blood tests, completion time or TAT (turn around time), and creates discomfort for patients due to repeated sampling. Hemolysis can occur in vitro or in vivo. In vitro hemolysis can occur due to lysis of red blood cells during the sample collection and handling of blood samples. In vitro hemolysis is a result of pre-analytical causes associated with sample collection, jarring transportation methods, extreme temperature, sample handling, delayed processing, and prolonged storage. In vivo hemolysis occurs if the rate of erythrocyte damage increases, thereby reducing the life span of erythrocytes. Hemolysis results in a decrease in the number of RBCs and HCT values due to lysis. Hematological instruments usually lyse the sample before measuring the HGB, the number of PLT, the number of WBC, and the number of WBC differential cells, so these values are usually not affected by hemolysis. In samples with hemolysis, the ESRvalues will decrease. In the ESR test with the Westergreen method, it is often found that hemolysis samples are difficult to assess because of unclear boundaries. The mechanism for shortening APTT in hemolysis samples has not been confirmed. This is thought to be caused by the release of phospholipids from erythrocytes and intracellular substances from leukocytes and platelets which can activate Cascade coagulation. Other literature states that activating the freezing cascade will cause PPT shortening and decreased fibrinogen levels, whereas APTT can extend or shorten the levels depending on whether there is activation or loss of fibrinogen. Hemolysis samples that are immediately examined may experience coagulation activation, so the APTT results are shortened.
NaturalProofs: Mathematical Theorem Proving in Natural Language

Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning. As a step in this direction, we develop NaturalProofs, a multi-domain corpus of mathematical statements and their proofs, written in natural mathematical language. NaturalProofs unifies broad coverage, deep coverage, and low-resource mathematical sources, allowing for evaluating both in-distribution and zero-shot generalization. Using NaturalProofs, we benchmark strong neural methods on mathematical reference retrieval and generation tasks which test a system's ability to determine key results that appear in a proof. Large-scale sequence models show promise compared to classical information retrieval methods, yet their performance and out-of-domain generalization leave substantial room for improvement. NaturalProofs opens many avenues for research on challenging mathematical tasks.
THE SUSTAINABLE DEVELOPMENT AND THE QUALITY EFFORT OF BUREAU OF STATISTICS AND CENSUS POPULATION LIBYA ON LIBYAN UNIVERSITIES: LITERATURE REVIEW

The current study aims to evaluate the sustainable development and the quality effort of Bureau of statistics and census population Libya on Libyan universities. To achieve the aim of the study the researcher relied on the descriptive-analytical method, and used the survey as the main tool for the study, the study sample includes 45 employees of Bureau of statistics and census population Libya. The results revealed that, on the existence of an average level of practices of the total quality strategy in the Bureau of statistics and census population Libya, meaning that the institution is working to improve its performance in an average way with regard to its focus and attention to its customers. It also conducts seminars and training courses for workers in order to consolidate the principles of quality efforts and adopt new methods and methods.
TU34. ASSOCIATION BETWEEN DEPRESSION DIAGNOSIS, POLYGENIC RISK FOR DEPRESSION AND ANTIHYPERTENSIVE MEDICATION NON-PERSISTENCE

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new model to recognize seizure states (e.g., ictal, preictal, interictal) from EEG in the IoT framework to monitor patients remotely. The proposed model uses an efficient hybrid genetic whale optimization algorithm (GWOA) based on naïve Bayes (NB-GWOA) for feature selection, and an adaptive extreme learning machine (ELM) based on a differential evolutionary (DE) algorithm (DEELM) for classification. In the NB-GWOA method, the genetic algorithm serves to enhance the exploitation of the whale optimization algorithm in the search of the optimal solutions, while the naïve Bayes method is used to determine a fitness function to assess every agent in the search space. GWOA has strong robustness and is capable of finding the best solutions in less than five iterations, so it is suitable for selecting discriminative features from a huge number of neurofeatures obtained from EEG. Further, the classification model is constructed based on ELM, which uses the DE algorithm for a fast and efficient learning solution. Results show that the proposed NB-GWOA-DEELM model can avoid over- and under-fitting and can provide better and more accurate performance in classifying seizure states from EEG than its competitors.
Adaptive Underwater Image Enhancement via Color Channel Compensation Based on Optical Restoration and Fusion

As human beings continue their way to find more and more resources beneath water and ocean, it becomes more urgent to have a very clear and detailed underwater image for us to explore the world unseen in the water. However, with the light propagating into the water, it is absorbed and scattered along the way which makes the underwater image unclear, hazy, detail-lost and color-shifted. Obviously, it is not the image we wish for. In the paper, the proposed method aims to enhance underwater image adaptively via color channel compensation based on optical image model. In the beginning the underwater image is restored only in green channel aiming to reduce the haze effect, then an adaptive color channel compensation is applied to correct the shifted color, lastly a multi-scale fusion is executed to show more image details after a white balance operation. Going for massive experiments, the proposed adaptive method fits in versatile scenes adaptively of greenish, bluish and turbid water body producing eye-friendly haze cover removed, color shift corrected, detail enhanced clear result image. Particularly the proposed method highly reduces the reddish effect after execution compared to many other state of art underwater image enhancement algorithm, while quantitatively the proposed method gives a better score too by underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE).
Physics-based Differentiable Depth Sensor Simulation.

Gradient-based algorithms are crucial to modern computer-vision and graphics applications, enabling learning-based optimization and inverse problems. For example, photorealistic differentiable rendering pipelines for color images have been proven highly valuable to applications aiming to map 2D and 3D domains. However, to the best of our knowledge, no effort has been made so far towards extending these gradient-based methods to the generation of depth (2.5D) images, as simulating structured-light depth sensors implies solving complex light transport and stereo-matching problems. In this paper, we introduce a novel end-to-end differentiable simulation pipeline for the generation of realistic 2.5D scans, built on physics-based 3D rendering and custom block-matching algorithms. Each module can be differentiated w.r.t sensor and scene parameters; e.g., to automatically tune the simulation for new devices over some provided scans or to leverage the pipeline as a 3D-to-2.5D transformer within larger computer-vision applications. Applied to the training of deep-learning methods for various depth-based recognition tasks (classification, pose estimation, semantic segmentation), our simulation greatly improves the performance of the resulting models on real scans, thereby demonstrating the fidelity and value of its synthetic depth data compared to previous static simulations and learning-based domain adaptation schemes.
Swarm Features Applied in Deep Learning for CBIR of Chest X-Ray Images

The chest image retrieval process involves searching entire images in database for accurate image retrieval. The image retrieval process consumes hardware resource and time depending on query image feature complexity, and database size. Traditional algorithms such as neural network and genetic algorithm consume more time for image retrieval due to hardware limitation and consumes more time for training neural network. In this paper, we propose Particle Swarm Optimization (PSO) and Capsulenet deep learning framework to minimize time consumption for Content Based Image Retrieval (CBIR) and improve precision, recall of retrieved images. The PSO-Capsulenet determines feature vectors of Chest X-ray image. The feature vectors of image obtain by evaluating color matrix histogram, shape and color features of X-ray images. The PSO-Capsulenet minimizes time consumed for image retrieval by 20% and improves retrieved image accuracy by 40% compared to traditional algorithms.
Evaluating the diagnostic accuracy of birads assessment of mammography and ultrasound according to ultrasound-guided core needle biopsy: 9 years-experience in a small town

Aim: To examine the diagnostic accuracy of the BI-RADS (Breast Imaging Reporting and Data System) assessment of ultrasonography and/or mammography according to US-guided core needle biopsy.Materials and Methods: 463 patients who had ultrasonography and/or mammography imaging and subsequent US-guided core needle biopsy between June 2011 and June 2020 in the Radiology Departments of two different centers in our town were studied retrospectively. BI-RADS assessment of ultrasonography and/or mammography were compared with histopathological diagnoses. When both examination existed, the higher score was determined as the final BIRADS category. The diagnostic efficiency of ultrasonography and/or mammography for determining malignancy were calculated. Results: Of the 463 lesions 222 (47.9%) were malignant and 241(52.1%) were benign. Based on ultrasonographic examination, malignancy was observed in 5 of the 86 (5.8%), 89 of 178 (50%), 4 of 63 (6.3%), 26 of 26 (100%) and 86 of 86 (100%), cases reported as BI-RADS 3, 4, 4A, 4C and 5. Based on mammography results, all of 50 masses (100%) classified as BI-RADS 5 and 38 of 47 masses (80.8%) classified as BI-RADS 4 were malignant. The frequency of malignancy in BI-RADS 3 masses was 5.8% based on ultrasonographic examination and 0.7% with both modalities. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for ultrasonography were 92.2, 61.0, 69.1, 89.2, and 76.2%; for mammography were 96.7, 41.1, 89.8, 70.0, and 88.0%; for combined radiological evaluation were 99.5, 53.5, 66.3, 99.2 and 75.5% respectively. Conclusion: When mammography and ultrasonography were evaluated together, the rate of malignancy in BIRADS 3 lesions decreased from 5.8% (only ultrasonography) to 0.7%. In addition, sensitivity and negative predictive value increased statistically significantly (p<0.001). This finding showed the importance of the co-evaluation of mammography and ultrasonography.
Investigation of Cloud Computing Security Issues & Challenges

The Trends of using cloud computing in IT industries is increasing since the Inception of this innovative technology.This new shift in the industrial technology, which will grow and develop continuously their e-governance in the coming few years.Most of the enterprises are migrating towards the cloud-computing environment rapidly due to its various advantages over traditional one.As per cisco, more than 93% workloads will be processed by Cloud data centers in 2021.Even several republics have already initiated to shape their own governmental public cloud around the globe.Phishing attempts affected the majority of educational establishments (60 percent) and account compromise (33 percent) in 2020.According to Gartner, consumers are responsible for more than 95 percent of cloud security flaws.Increasing frequent cyber-attacks potentially cause significant material damage if no special security measures are taken.The objective here is to investigate the security issues & challenges in terms of cloud specific vulnerabilities & threats using real time experimental setup with vulnerability scanner which further provide an ease to applications deployment.
An Analysis of Students’ Errors in Writting Descriptive Paragraph

The purpose of this study is to examine the language error in student’s descriptive paragraph writing of the third semester students of English Department University of Muhammadiyah Banjarmasin. This study focused mainly on sentences because the researcher found that errors in EFL students’ sentences contruction may lead to miscommunication. Ten pieces of descriptive paragraph writing were collected and analyzed. The results showed that the most frequently committed errors were article, to be, word order, subject-verb agreement, spelling and grammar. It is suggested that the students study more in how to develop a sentence in English to avoid error in writing. This research also implies that explicit feedback on students’ writing errors is genuinely needed.&#x0D;
The modern science of acmeology: integration of psychology and pedagogy in education

The purpose of this study is to analyse and define the structural and substantive characteristics of acmeological categories (terms) with regard to fundamental education. The authors carried out theoretic and historical, methodological, systemic and logical analysis of acmeological, psychological and pedagogical studies on efficiency of education system and conceptual framework of creative professional and educational activities. Results of studying and substantiating of acmeological categories (terms) of creative activities within the system of fundamental education are presented in detail. Their structural and substantive features are identified and classified. The study presents categorization of acmeological concepts that integrate psychology and pedagogy. Results of this study can be used 1) as a scientific and methodological basis of fundamental education, particularly for vocational creative activities and for teaching the acme of productivity and professionalism; 2) for classification of acmeological categories and their incorporation into the conceptual framework of the Russian fundamental education.