Reviewerly/exHarmony huggingface dataset:
A Nomogram Predicting Early Cervical Cancer Distant Recurrence

<h3>Purpose/Objective(s)</h3> Distant recurrence is the most common cause of treatment failure in locally advanced cervical cancer (LACC) following chemoradiation (CRT). We explored risk factors and developed a clinical nomogram predicting distant recurrence within 6 months of CRT completion. <h3>Materials/Methods</h3> This was a retrospective case-control study of LACC patients treated with definitive CRT at a tertiary academic center from 1997-2020. Patients underwent FDG-PET at initial staging and approximately 3 months post treatment and had ≥ 3-month clinical follow-up. Patients were prescribed 50.4 Gy external beam radiation (EBRT) to the pelvic ± para-aortic (PA) lymph nodes (PA if involved on PET staging); both brachytherapy and cisplatin were delivered concurrently with EBRT. Patients who developed distant recurrence within 6 months were designated as cases and all others as controls. The dataset was randomly divided into 2/3 training and 1/3 testing. Binary logistic regression was applied to training data to determine significant predictor variables. Continuous variables were also assessed as categorical variables with cut points determined by Youden's method. Patients with missing data for significant predictors were excluded (110 training, 54 testing). The best model was defined by highest c-statistic and was applied to the testing data set. <h3>Results</h3> 514 patients were identified of whom 45 (9%) developed distant recurrence within 6 months of CRT completion (15 biopsy proven, 30 clinically determined); 44 (98%) identified on 3 month post treatment PET. The dataset was divided into training (20 cases, 214 controls) and testing (10 cases, 106 controls). Age, treatment duration, histology (87% squamous), cervix SUV<sub>max</sub>, pre/post squamous cell antigen did not differ across cases and controls in both training and testing data. Logistic regression applied to training data showed histology grade (poorly vs well/mod differentiated), PET positive lymph nodes (pelvic/para-aortic vs none) and PET initial tumor volume (≥50 vs < 50 cm<sup>3</sup>) had the most predictive multivariate beta coefficients (1.2, <i>P</i> = 0.025; 1.2, <i>P</i> = 0.016; 1.3, <i>P</i> = 0.028 respectively) with an overall c-statistic of 0.76. In the nomogram, each variable was assigned one point resulting in a total score range of 0-3 points, with higher scores indicating increasing risk. 3 points carried 29% positive predictive value (PPV), and 0 points indicated 100% negative predictive value (NPV) (Table 1). This nomogram was applied to the testing data and showed c-statistic 0.82 and 3 points PPV of 27% and 0 points NPV of 100%. <h3>Conclusion</h3> More than a quarter of LACC patients with poorly differentiated histology, PET positive lymph nodes, and initial primary tumor volume greater than 50 cm<sup>3</sup> experience distant recurrence within 6 months of completing CRT.
Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware

We give an overview of the different rendering methods and we demonstrate that the use of a Generative Adversarial Networks (GAN) for Global Illumination (GI) gives a superior quality rendered image to that of a rasterisations image. We utilise the Pix2Pix architecture and specify the hyper-parameters and methodology used to mimic ray-traced images from a set of input features. We also demonstrate that the GANs quality is comparable to the quality of the ray-traced images, but is able to produce the image, at a fraction of the time. Source Code: https://github.com/Jaredrhd/Global-Illumination-using-Pix2Pix-GAN
Learning Vietnamese-English Code-Switching Speech Synthesis Model Under Limited Code-Switched Data Scenario

Recent advances in deep learning facilitate the development of end-to-end Vietnamese text-to-speech (TTS) systems that produce Vietnamese voices with high intelligibility and naturalness. However, enabling these systems to speak Vietnamese and English words in the same utterance fluently remains a challenge known as the code-switching (CS) problem in speech synthesis. The main reason is that it is not easy to obtain a large amount of high-quality CS corpus from a Vietnamese speaker. In this paper, we explore the efficacy of three approaches, which are based on the Tacotron-2 end-to-end framework, to build such a Vietnamese TTS system under a limited code-switched data scenario: (1) CS synthesis based on grapheme-to-syllable (G2S), (2) CS synthesis based on speaker embedding, and (3) CS synthesis based on speaker embedding and language embedding. We handle English and Vietnamese words in the code-switched input text by converting them into Vietnamese syllables using our G2S model. For the speaker-embedding based approach, we combine Vietnamese monolingual data in our dataset with an English public dataset to train a multi-speaker Tacotron-2 system. The experimental results show that adding language embedding is effective, and training with character input representations outperforms phonemes. Thus, the speaker and language-embedding based approach achieves strong results in naturalness for CS speech. Besides, the G2S-based CS synthesis also has good results, with almost absolute English pronunciation accuracy.
Virtual Training Environment for Gas Operatives: System Usability and Sense of Presence Evaluation

Training of gas operators in real-life settings often has associated risks to health and property.The use of a virtual environment to train gas operators has the potential to offer risk-free training.This study tests the usability of a virtual environment specifically designed to teach new gas operatives in near real-life scenarios.Thirty-two participants tested the virtual environment and performed different tasks required to complete gas safety checks.We used SUS (System Usability Scale) and sense of presence questionnaires to collect data from these participants.The SUS analysis indicated that most participants belonging to a different gender, age, and virtual reality experience groups were comfortable in the VR training environment.The sense of presence data analysis also confirmed similar results as all sense of presence factors scored high regardless of the demographics characteristics of the participants.However, there is still a need to add different scenarios to make the virtual environment into a comprehensive training course.
Using Mixed-Frequency and Realized Measures in Quantile Regression

Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low- (for macro-variables) and high-frequency (which may include an "-X" term related to realized volatility measures) components. The quality of the suggested quantile regression model, labeled MF-Q-ARCH-X, is assessed in a number of directions: we derive weak stationarity properties, we investigate its finite sample properties by means of a Monte Carlo exercise and we apply it on financial real data. VaR forecast performances are evaluated by backtesting and Model Confidence Set inclusion among competitors, showing that the MF-Q-ARCH-X has a consistently accurate forecasting capability.
Attention Guided Spatio-Temporal Artifacts Extraction for Deepfake Detection

Recently, deep-learning based model has been widely used for deepfake video detection due to its effectiveness in artifacts extraction. Most of the existing deep-learning detection methods with the attention mechanism attach more importance to the information in the spatial domain. However, the discrepancy of different frames is also important and should pay different levels of attention to temporal regions. To address this problem, this paper proposes an Attention Guided LSTM Network (AGLNet), which takes into consideration the mutual correlations in both temporal and spatial domains to effectively capture the artifacts in deepfake videos. In particular, sequential feature maps extracted from convolution and fully-connected layers of the convolutional neural network are receptively fed into the attention guided LSTM module to learn soft spatio-temporal assignment weights, which help aggregate not only detailed spatial information but also temporal information from consecutive video frames. Experiments on FaceForensics++ and Celeb-DF datasets demonstrate the superiority of the proposed AGLNet model in exploring the spatio-temporal artifacts extraction.
Robolution: Real Time Predictive Analytics for Industrial Robots

Robots have been playing a very important role in our day-to-day lives and will be a necessity in the coming future. Whenever we hear automation, the first thing that strikes our mind is a robot performing the given task. But if a robot fails to do the task, it could cost an individual or corporate a huge financial loss. In this study, we have learned the working of various robots and drawbacks that hold them back. For this work, we did make a study of drives used in the robot and after that applied the machine learning algorithms to predict the classification of whether the robot will function properly or not, based on the data of drive(s).
Big Data Curriculum Design and Teaching Practice Based on Smart Learning Environment

This article proposes a smart learning system model based on big data.The system model is based on a big data architecture, combined with an adaptive general learning model, and provides information assistance and technical support for learners and teachers to learn through education big data and learning analysis technology.And based on the overall design framework, a learning early warning system model based on multi-modal data and an intervention mechanism process model are proposed.Experimental research shows that the intelligent learning system model can solve teaching resource management and the construction and application of various online courses.Help schools improve teaching quality, improve teaching efficiency, simplify teaching management, and promote teaching reform.
Selection of the Best Lecturer in Management Forum Indonesia Chapter DKI Jakarta with Analytical Hierarchy Process (AHP) Method

This study aims to be able to provide recommendations in the decision-making system related to the best lecturers at the Jakarta Chapter of the Indonesian Management Lecturer Forum. It is hoped that the research results can be used as input in the Best Lecturer decision making system with a more systematic system. This research was conducted through interviews, questionnaires and literature study related to the Lecturer profession. In the field, the assessment criteria are still rough, therefore this study must include all the criteria into the mechanism, rules, and codes. Analysis, each criterion is analyzed using the AHP (Analytical Hierarchy Process) method in weighting. The cumulative results of the recapitulation of scores show that teaching is still a top priority in the implementation of the Tridaharma of Lecturers, after that the second priority is research, then community service and finally support in the form of participation in supporting activities and scientific development.
Analysis: The Enlightenment of Transformational Curriculum Leadership to the Management of School Physical Education Curriculum in my country

The article aims to explore the new concepts and their enlightenment to school physical education curriculum management based on the concept of transformative curriculum leadership.The study found that transformational curriculum leadership is an educational concept that conforms to the trend of the times and produces a courage to criticize and reflect.It is practice-oriented and implementation-oriented, and faces the complexity and irreducibility of school education.Although it is not as clear as the traditional curriculum leadership view, it is closer to the real education situation, which has revolutionary inspiration for the transformation of school physical education curriculum management.Its enlightenment to the management of physical education curriculum in our country mainly includes the following three points: ( 1) Re-propose a new definition of the concept of leadership in physical education curriculum; (2) Combine sports and health to show new art; (3) Combine school sports culture with local characteristics Establish harmonious school public relations.
Multi-branch Graph Network for Learning Human-Object Interaction

In this work, we study the task of detecting human-object interactions (HOI) from images, which is defined as detecting triplets of (human, predicate, object). A common practice in the literatures is firstly localizing human and object instances and then inferring the triplets or predicates only as a classification task from the detected human-object pairs. A data sparsity issue arises when inferring the triplets because of the serious data imbalance among HOI classes, while a data variance issue arises when inferring predicates only since a predicate can carry different semantic meanings when being applied to different objects. To resolve the problem, we propose to decompose HOI classes with a same predicate into several semantic groups based on the appearance, semantic information and function of the objects. By doing this, semantic-related HOI classes are grouped together to compensate the data sparsity issue, while visually and functionally less related HOI classes are separated to relieve the data variance issue. We reveal multiple levels of decomposition in different granularities can provide richer auxiliary information to boost the performance. We implement this idea with a multi-branch graph network, while the multiple branches make classifications based on different levels of decompositions. We evaluate our method on popular HICO-Det dataset. Experimental results show that our method achieves state-of-the art performance.
CNN-based character recognition for a contextless text input system in immersive VR

The paper investigates the applicability of deep neural networks and transfer learning procedure to developing a handwriting recognition system for typing contextless content in a Virtual Reality system. Inputting special characters in such systems is still a nontrivial task, even nowadays. Most algorithms for editing text focus on contextual text recognition from speech or gestures. Unfortunately, these approaches fail for text containing not only letters additionally in the context of words, phrases, and trivial punctuation characters. In the paper, the authors explore the possibility of typing more advanced structures, which for example, can be observed in source code editing. The aim of the paper is to verify if modern machine learning algorithms can facilitate text typing in a context-free environment. The authors have chosen a transfer learning procedure utilizing already trained convolutional layers from ALEXNET [1], followed by deep network classification layers. The methods and procedures implemented for the Virtual Reality system used to acquire data necessary to train the recognition model are also presented.
Assessment of Dynamic Open-source Cross-site Scripting Filters for Web Application

This study investigates open-source dynamic XSS filters used as security devices in web applications to account for the effectiveness of filters in protecting against XSS attacks.The experiment involves twelve representative filters, which are examined individually by placing them into the final output function of a custom-built single-input-form web application.To assess the effectiveness of the filters in their tasks of sanitizing XSS payloads and in preserving benign payloads, a black-box testing method is applied using an automated XSS testing framework.The result in working with malicious and benign payloads shows an important trade-off in the filters' tasks.Because the filters that only check for dangerous or safe elements, they seem to neglect to validate their values.As some safe values are mistreated as dangerous elements, their benign payload function is lost in the way.For the filters to be more effective, it is suggested that they should be able to validate the respective values of malicious and benign payloads; thus, minimizing the trade-off.This particular assessment of XSS filters provides important insight regarding the filters that can be used to mitigate threats, including the possible configurations to improve them in handling both malicious and benign payloads.
Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory

Machine translation has developed rapidly. But there are some problems in machine translation, such as good reading, unable to reflect the mood and context, and even some language machines can not recognize. In order to improve the quality of translation, this paper uses the SSCI method to improve the quality of translation. It is found that the translation quality of hierarchical phrases is significantly improved after using the parallel algorithm of machine translation, which is about 9% higher than before, and the problem of context free grammar is also solved. The research also found that the use of parallel algorithm can effectively reduce the network memory occupation, the original 10 character content, after using the parallel algorithm, only need to occupy 8 characters, the optimization reaches 20%. This means that the parallel algorithm of hierarchical phrase machine translation based on distributed network memory can play a very important role in machine translation.
High Capacity Reversible Steganography on CMY and HSI Color Images Using Image Interpolation

Steganography using image interpolation has created a new research area in multimedia communication. A reversible data concealing in HSI and CMY color models using image interpolation is proposed in this paper. The HSI and CMY image models are interpolated using High Capacity Reversible Steganography (CRS) technique. The median plane of both HSI and CMY color models are selected for secret message bit concealing. The secret message bits are concealed in the cover plane by Exclusive OR (XOR) operation. Since the cover image is recovered after secret message bit retrieval, this finds application in military and medical imaging applications. The experimental results of proposed scheme showed very high embedding capacity of about 16 bits in each pixel location of calculated pixel value, good image quality with a surface similarity index measure (SSIM) value 1 and high PSNR. Also, high robustness is achieved on comparing with the existing works.
Privacy-Preserving Association Rule Mining for Electronic Health Record

Today trend of electronic health record (EHR) system has increased in the hospitals. The huge amount of data generated and stored electronically in the hospitals, which store patient’s records. The data store in an EHR system is important property for the treatment of patients. Association rule mining is one of the data mining techniques and used to extract hidden patterns from large dataset. Association mining is helpful in various applications of healthcare sector (e.g., relationship between symptoms and disease, predicting risks of disease based on the historical data, and correlation between treatments and disease etc.). Presently in distributed environment, patient’s data are share and distributed between medical experts for better treatment. Information and communication technology provided various advantages for storing, updating and retrieving relevant data, whenever required for its stakeholders. Privacy preserving data mining techniques ensures and preserves the confidential patterns or restricted data values to unauthorized users. Association rule mining is more accurate if database are stored in local hospital but requirement has been changed in the ICT era. Sharing EHR breaches privacy of patient’s sensitive record. Hence, Electronic Health Records is now more emphasized on security and privacy issues and association rules are one of many techniques to maintain the patient’s records. In this paper, we proposed a modified PPDARM method for preserving privacy and associate it with RSA algorithms in distributed EHRs systems.
Factors Influencing Individual and Organizational Adoption of Cryptocurrencies

Cryptocurrencies have gathered a lot of attention since the birth of Bitcoin in 2009. However, less is known about how the characteristics of cryptocurrencies and other factors facilitate and impede their adoption. The objective of this study is to discuss the main conceptual findings on individual and organizational adoption of an innovation and examine the factors influencing the adoption of cryptocurrencies from a holistic perspective. This study proposes two theoretical models. The first model presents the factors impacting the adoption of cryptocurrencies at the individual level. It was derived by combining the eight factors of the Innovation Diffusion Theory (IDT) model with the construct “personal innovativeness.” The second model presents the factors impacting the adoption of cryptocurrencies at the organizational level. It was developed based on the Technology–Organization–Environment (TOE) framework. This research allows a better understanding of the current outlook of the cryptocurrency system and predicts its future perspectives. Therefore, this chapter is practically useful for those cryptocurrency users, traders and merchants considering their implementation as an alternative payment method.
eRequirement Elicitation Techniques for a C-Programming Learning Application

Requirement elicitation is a part of the application development process which determines the functional and non-functional requirements of the application. This study has elicited the requirements of a programming learning application by using several requirement elicitation techniques. The purpose of the application is to assist novice programmers in learning C language programming. The requirement elicitation was done by using qualitative approaches in a triangulation strategy. The triangulation involved literature reviews on related existing C-programming applications and semi-structured interviews with five expert programming lecturers in Malaysian Polytechnic. The requirement elicitation has identified an important approach of a programming learning application which is the programming visualization to help novices understand the program execution behaviour better. It has determined that the application should visualize the variable contents and program execution steps as its functional requirements. Meanwhile, the non-functional requirement of the application is that the application should be designed to be a simple IDE-based application since it is targeting the novices. The finding from this requirement elicitation is essential in determining important functions to be available in the developed application and how it is supposed to be implemented. This application could enhance programming skills of the students and prepare them to be competent programmers for the future industrial demand.