Reviewerly/exHarmony huggingface dataset:
Optimized Conversion of Categorical and Numerical Features in Machine Learning Models

While some data have an explicit, numerical form, many other data, such as gender or nationality, do not typically use numbers and are referred to as categorical data. Thus, machine learning algorithms need a way of representing categorical information numerically in order to be able to analyze them. Our project specifically focuses on optimizing the conversion of categorical features to a numerical form in order to maximize the effectiveness of various machine learning models. From the methods utilized, it has been observed that wide and deep is the most effective model for datasets that contain high-cardinality features, as opposed to learn embedding and one-hot encoding.
Chinese named entity recognition method for the field of network security based on RoBERTa

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.
Machine Learning Fairness is Computationally Difficult and Algorithmically Unsatisfactorily Solved

The main purpose of the paper is to analyze the computational difficulties of selecting the suitable classification algorithms that satisfy specific ethical criteria, when real data is used in training. Employing an imbalanced credit decision dataset largely used for credit scoring and applying a set of algorithms and several fairness criteria, we show that many typical classification algorithms do not satisfy in a reasonable manner more than one fairness criterion when considering more than one protected attribute. This adds a layer of difficulty to the ones represented by the need of large databases and data- and computationally-intensive decision-making systems as used in domains such as credit scoring and hiring. A novel analysis of this study is directly relating ML/AI fairness criteria and computational complexity. We reframe the problem of complexity by connecting it to the search of an ethically acceptable solution instead of just an accurate solution. The results suggest the continued need for human input in fairness decisions, especially when deciding tradeoffs between fairness criteria.
Towards Effective Patient Simulators

In this paper we give an overview of the field of patient simulators and provide qualitative and quantitative comparison of different modeling and simulation approaches. Simulators can be used to train human caregivers but also to develop and optimize algorithms for clinical decision support applications and test and validate interventions. In this paper we introduce three novel patient simulators with different levels of representational accuracy: HeartPole, a simplistic transparent rule-based system, GraphSim, a graph-based model trained on intensive care data, and Auto-ALS-an adjusted version of an educational software package used for training junior healthcare professionals. We provide a qualitative and quantitative comparison of the previously existing as well as proposed simulators.
Application of Machine Learning Algorithms in Predicting Pyrolytic Analysis Result

Abstract Geochemical studies of organic matter in source rocks play an important role in predicting the oil and gas accumulation of any territory, especially in oil and gas shale. For deep understanding, pyrolytic analyses are often carried out on samples before and after extraction of hydrocarbon with chloroform. However, extraction is a laborious and time-consuming process and the workload of laboratory equipment and time doubles. In this work, machine learning regression algorithms is applied for forecasting S2ex based on the pyrolytic analytic result of non-extracted samples. This study is carried out using more than 300 samples from 3 different wells in Bazhenov formation, Western Siberia. For developing a prediction model, 5 different machine learning regression algorithms including Multiple Linear Regression, Polynomial Regression, Support vector regression, Decision tree and Random forest have been tested and compared. The performance of these algorithms is examined by R-squared coefficient. The data of the X2 well was used for building a model. Simultaneously, this data is divided into 2 parts – 80% for training and 20% for checking. The model also was used for prediction of wells X1 and X3. Then, these predictive results were compared with the real results, which had been obtained from standard experiments. Despite limited amount of data, the result exceeded all expectations. The result of prediction also showcases that the relationship between before and after extraction parameters are complex and non-linear. The proof is R2 value of Multiple Linear Regression and Polynomial Regression is negative, which means the model is broken. However, Random forest and Decision tree give us a good performance. With the same algorithms, we can apply for prediction all geochemical parameters by depth or utilize them for well-logging data.
Translating sign language videos to talking faces

Communication with the deaf community relies profoundly on the interpretation of sign languages performed by the signers. In light of the recent breakthroughs in sign language translations, we propose a pipeline that we term "Translating Sign Language Videos to Talking Faces". In this context, we improve the existing sign language translation systems by using POS tags to improve language modeling. We further extend the challenge to develop a system that can interpret a video from a signer to an avatar speaking in spoken languages. We focus on the translation systems that attempt to translate sign languages to text without glosses, an expensive annotation form. We critically analyze two state-of-the-art architectures, and based on their limitations, we improvise the systems. We propose a two-stage approach to translate sign language into intermediate text followed by a language model to get the final predictions. Quantitative evaluations on the challenging benchmarks on RWTH-PHOENIX-Weather 2014 T show that the translation accuracy of the texts generated by our translation model improves the state-of-the-art models by approximately 3 points. We then build a working text to talking face generation pipeline by bringing together multiple existing modules. The overall pipeline is capable of generating talking face videos with speech from sign language poses. Additional materials about this project including the codes and a demo video can be found in https://seshadri-c.github.io/SLV2TF/
But how robust is RoBERTa actually?: A Benchmark of SOTA Transformer Networks for Sexual Harassment Detection on Twitter

Harassment, which is of sexual/physical in nature, is defined as any unwanted sexual misconduct, including the unwarranted and ill-suited promise of benefit in exchange for sexual indulgence. It also includes a span of actions from verbal misdemeanours to sexual exploitation which can occur in multiple social settings resulting in the victim to feel intimidated, ill-treated or uncomfortable. Such content in the cyber space can easily be detected and thereby, used to condone such atrocious and illegal behaviour and ban users for conducting the same, without having the need to be reported to get penalised and thus, make the platform free from such predatory behaviour. In this paper, we have worked with transformer architecture RoBERTa to understand and interpret context of such tweets better and also conducted an extensive comparison of other state-of-the-art transformer architectures.
Performance Maintenance of Machine Learning-based Emergency Patient Mortality Predictive Models

Machine learning provides a flexible technique to predict the survival of patients who are admitted to hospital as emergency admissions. Mortality prediction is a central component of emergency patient quality of care and this can act as an indicator of severity to determine who needs prioritized care. Machine learning-based models, as opposed to human-crafted severity score systems, allow for much more complex and updateable models to be developed based on a larger set of input data attributes. While various studies of machine learning-based predictive models for predicting inpatient mortality have been carried out there is little literature on performance maintenance of these models. Determining the performance maintenance of these models over time determines how reliably they can be utilized into the future and for how long. The best performing model in this study achieve&#x2019;s an AUC of 0.86 upon training and is able to maintain a similarly high AUC of 0.845 as of the end of the period of performance maintenance evaluation nine months later. This is the first paper that the authors are aware of to consider and measure relative performance maintenance of machine learning-based models for emergency admission mortality prediction.
Designing Embodied Virtual Agent in E-commerce System Recommendations using Conversational Design Interaction

System recommendation is currently on the rise: more and more e-commerce rely on this feature to give more privilege to their users. However, system recommendation still faces a lot of problems that can lead to its downfall. For instance, the cold start problem and lack of privacy for user’s data in system recommendation will make the quality of this system lesser than ever. Moreover, e-commerce also faces another significant issue which is the lack of social presence. Compared to offline shopping, online shopping in e-commerce may be seen as lacking human presence and sociability as it is more impersonal, cold, automated, and generally devoid of face-to-face interactions. Hence, all of those issues mentioned above may lead to the regression of user’s trust toward e-commerce itself. This study will focus on solving those problems using conversational design interaction in the form of a Virtual Agent. This Virtual Agent can help e-commerce gather user preferences and give clear and direct information regarding the use of user’s data as well as help the user find products, promo, or similar products that they seek in e-commerce. The final result of this solution is a high fidelity prototype designed using User-Centered Design Methodology and Natural Conversational Framework. The implementation of this solution is carried out in Shopee e-commerce by modifying their product recommendation system. This prototype was measured using the usability testing method for usability goals efficient to use and user experience goals helpful.
Optimized Yolov3 Deployment on Jetson TX2 With Pruning and Quantization

Pruning and Quantization are commonly techniques deployed on deep convolutional networks to accelerate the model and reducing size, taking the resolution as a sacrifice. In embedded systems where computation power is limited due to power and cost constraints, pruning and quantization is mandatory for such networks especially where real-time processing is required, such as image classification in live video streams. In this paper, a pruned and quantized YOLOv3 model is deployed on Nvidia's industrial standard model Jetson TX2, which demonstrated an increase of 5 FPS in image classification via an 640×480 USB camera, while allocating only 16.7% storage on disk.
A Wireless Time-Scaling Chaotic Shift Keying Encryption System For Biosensing Systems

This work presents a wireless time-scaling chaotic shift keying encryption system that can be used in wireless body area network applications. In wireless sensor nodes, the communication protocol being used provides some security measures and is implemented in software. However, no additional security measures are usually implemented. This paper demonstrates a discrete level real time encryption system using analog circuitry on a printed circuit board. The encryption system uses op amps, multipliers and resistors to implement the encryption. To implement wireless capabilities, commercial wireless microcontrollers using Bluetooth Low Energy were added, and a custom Bluetooth Low Energy profile was created to stream the analog encrypted signal.Clinical relevance- This work demonstrates an encryption system for wireless sensor devices for improved protection of private health information.
Robust clustering of functional directional data

Abstract A robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem where it is applied to cluster aircraft trajectories.
Effectiveness of Data Augmentation in Multi-class Face Recognition

Recent research shows that classification results of using deep learning methods provide better accuracy than those of using machine learning techniques. However, these results are highly dependent on the amount of data used. This study examines the effectiveness of data augmentation in improving the performance of convolutional neural network models, namely ResNet-50, MobileNetV3 Small, and MobileNetV3 Large. The performance is measured using the F1-score of the three models of classifying student faces. The experimental results indicate that the F1-score of the ResNet-50 architecture increases from 0.753 to 0.928 when trained using an augmented dataset. The accuracy of the models constructed using the MobileNetV3 Small and Large architectures increased from 0.685 to 0.920 and from 0.660 to 0.930, respectively, when trained using an augmented dataset. The accuracies of ResNet-50, MobileNetV3 Small, and MobileNetV3 Large when evaluated using the testing dataset were 0.865, 0.850, and 0.870, respectively.
A Zero-Watermark Scheme Based on Quaternion Generalized Fourier Descriptor for Multiple Images

Most of the existing zero-watermark schemes for medical images are only appropriate for a single grayscale image. When they protect a large number of medical images, repeating operations will cause a significant amount of time and storage costs. Hence, this paper proposes an efficient zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor (QGFD). Firstly, QGFD is utilized to compute the feature invariants of each color image, then the representative features of each image are selected, stacked, and reshaped to generate a feature matrix, which is then binarized to get a binary feature image. Copyright information can be converted into the copyright image by using QR code technology, which contains more information. Finally, the zero-watermark image is constructed by executing the XOR operation on the copyright image and the feature image scrambled by the Cat map. In the experiment, different parameters are selected to determine the maximum number of images that the proposed scheme can protect simultaneously while achieving good robustness. The experimental results demonstrate that the proposed scheme can effectively resist common attacks, geometric attacks and joint attacks, and effectively improve the operation efficiency of the algorithm, thus effectively decreasing the time and storage cost of copyright protection for lots of medical images.
Interpretive self-supervised pre-training

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, their application on medical data has been limited. In this work, we have proposed a novel loss function and derive it's asymptotic lower bound. We have also shown that self-supervised pre-training with the proposed loss function helps in surpassing the supervised baseline on the downstream task. We have also shown that the self-supervised pre-training helps a model in learning better representation in general to achieve better performance compared to supervised baselines. We have mathematically derived that the contrastive loss function asymptotically treats each sample as a separate class and works by maximizing the distance between any two samples and this helps to get better performance. Finally, through exhaustive experiments, we demonstrate that self-supervised pre-training helps to surpass the performance of fully supervised models on downstream tasks.
A detailed study on sentimental analysis using Twitter data with an Improved deep learning model

Under the present developments and current situation, the entire globe is changing fast. With the Internet being utilized in every sector, the internet has become a necessary requirement for everyone. With the quick expansion in informal community applications, individuals are utilizing these stages to voice their sentiments as to everyday issues. Assembling and investigating people's reactions toward purchasing an item, public administrations are essential. Sentiment analysis is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects [1]. As of late, analysts in the field of estimation examination have been worried about dissecting suppositions on various subjects, for example, films, business items, and day by day cultural issues. Twitter is a gigantically mainstream microblog on which customers may voice their assessments. Assessment examination of Twitter information is a field that has been given a lot of consideration in the course of the most recent decade and includes taking apart "tweets" and the substance of these articulations. In this paper, a deep learning model has been made with Embedding, CNN and LSTM layers. Then tweets from the web are collected for a particular topic using the Web Scraping technique by Twitter API and the overall sentiment is analyzed and a detailed sentiment report is made for that particular topic.
Real-time implementation of Fuzzy Logic Controller based on chicken swarm optimization for the ball and plate system

Abstract The ball and Plate (BaP) system is the typical example of the nonlinear dynamic system that is used in a wide range of engineering applications. So, many researchers in the control field are using the Bap system to check robust controllers under several points that challenge it, such as internal and external disturbances. Our manuscript proposed a position control intelligent technique with two directions (2D) for the BaP system by optimized multi Fuzzy Logic Controllers (FLC’s) with Chicken Swarm Optimization (CSO) for each one. The gains and rules of the FLC’s can tune based on the CSO. This proposal utilizes the ability of the FLC’s to observe the position of the ball. At our work, the BaP system that belonged to Control Laboratory/Systems and Control Engineering department is used for real-time proposal implementation. The results have been showing a very good percentage enhancement in settling time, rise time, and overshoot, of the X -axis and Y -axis, respectively.
Binary Harris Hawks Optimisation Filter Based Approach for Feature Selection

Feature Selection (FS) is a technique to reduce the dimensionality of datasets by eliminating irrelevant and redundant features to enhance the performance of the data mining tasks. Meta-heuristic algorithms are promising search engines to traverse the feature space to find a (near) optimal feature subset. Harris hawks optimization (HHO) algorithm is a recently developed meta-heuristic algorithm which is inspired from the hunting strategy of hawk in nature. The main contribution of this paper is that it proposes two new filter based methods for applying FS in classification problems. The methods integrate the information theory with an HHO algorithm. The first method applies the HHO with the mutual information between any two features. The second method applies the HHO with the entropy of each group of features. The adopted fitness function enhances the performance based on both the number of selected features and the classification accuracy. It gives different weights for relevance and redundancy. The results of the experiments show that with proper weights, the two proposed methods can significantly reduce the number of selected features and achieve a higher classification accuracy in most of the datasets. The first method usually selects a smaller feature subset, while the second method can achieve higher classification accuracy.
Learning discriminative and generalizable features with multi-branch for person re-identification

Finer-grained local features play a supplementary role in the description of pedestrian global features, and the combination of them has been an essential solution to improve discriminative performances in person re-identification (PReID) tasks. The existing part-based methods mostly extract representational semantic parts according to human visual habits or some prior knowledge and focus on spatial partition strategies but ignore the significant influence of channel information on PReID task. So, we proposed an end-to-end multi-branch network architecture (MCSN) jointing multi-level global fusion features, channel features and spatial features in this paper to better learn more diverse and discriminative pedestrian features. It is worth noting that the effect of multi-level fusion features on the performance of the model is taken into account when extracting global features. In addition, to enhance the stability of model training and the generalization ability of the model, the BNNeck and the joint loss function strategy are applied to all vector representation branches. Extensive comparative evaluations are conducted on three mainstream image-based evaluation protocols, including Market-1501, DukeMTMC-ReID and MSMT17, to validate the advantages of our proposed model, which outperforms previous state-of-the-art in ReID tasks.