Reviewerly/exHarmony huggingface dataset:
A two-stage modeling approach for breast cancer survivability prediction

Despite the increasing number of studies in breast cancer survival prediction, there is little attention put toward deceased patients and their survival lengths. Moreover, developing a model that is both accurate and interpretable remains a challenge. This paper proposes a two-stage data analytic framework, where Stage I classifies the survival and deceased statuses and Stage II predicts the number of survival months for deceased females with cancer. Since medical data are not entirely clean nor prepared for model development, we aim to show that data preparation can strengthen a simple Generalized Linear Model (GLM)1 to predict as accurate as the complex models like Extreme Gradient Boosting (XGB)2 and Multilayer Perceptron based on Artificial Neural Networks (MLP-ANNs)3 in both stages. In Stage I, we use recent Surveillance, Epidemiology, and End Results (SEER)4 data from 2004 to 2016 to predict short term survival statuses from 6-months to 3-years with 6 month increments. Synthetic Minority Over-sampling Technique (SMOTE),5 Relocating Safe-Level SMOTE (RSLS)6, Adaptive Synthetic (ADASYN)7 re-sampling techniques, Least Absolute Shrinkage and Selection Operator (LASSO)8 and Random Forest (RF)9 feature selection methods along with integer and one-hot encoding are combined with the three popular data mining methods: GLM, XGB, and MLP. In Stage II, we predict the number of survival months for patients who are correctly predicted as deceased within 3-years. Again, we employ GLM, XGB, and MLP for regression along with LASSO and RF for feature selection and one-hot encoding to encode the categorical features. We obtain Area Under the Receiver Operating Characteristic Curve (AUC)10 values of 0.900, 0.898, 0.877, 0.852, 0.852, and 0.858 for 6-month, 1-, 1.5-, 2-, 2.5, and 3-year survival time-points, respectively, using OneHotEncoding-GLM-LASSO-ADASYN. We use the change in the Odds Ratio values in GLM to manifest the impact of individual categorical levels and numerical features on the odds of death. In Stage II, we obtain Mean Absolute Error (MAE)11 of 7.960 months using OneHotEncoding-GLM-LASSO when predicting the number of survival months for deceased patients. We present the top contributing features and their coefficient values to illustrate how the presence of each feature alters the predicted number of survival months. To the best of our knowledge, this is the first study that implements both breast cancer survival classification and regression in a two-stage approach. All data-driven findings are presented in order to assist clinicians make better care decisions using GLM, an interpretable and computationally efficient method that predicts survival status and survival lengths for deceased patients, to help foster human and machine interactions.
OpenTera: A microservice architecture solution for rapid prototyping of robotic solutions to COVID-19 challenges in care facilities

As telecommunications technology progresses, telehealth frameworks are becoming more widely adopted in the context of long-term care (LTC) for older adults, both in care facilities and in homes. Today, robots could assist healthcare workers when they provide care to elderly patients, who constitute a particularly vulnerable population during the COVID-19 pandemic. Previous work on user-centered design of assistive technologies in LTC facilities for seniors has identified positive impacts. The need to deal with the effects of the COVID-19 pandemic emphasizes the benefits of this approach, but also highlights some new challenges for which robots could be interesting solutions to be deployed in LTC facilities. This requires customization of telecommunication and audio/video/data processing to address specific clinical requirements and needs. This paper presents OpenTera, an open source telehealth framework, aiming to facilitate prototyping of such solutions by software and robotic designers. Designed as a microservice-oriented platform, OpenTera is an end-to-end solution that employs a series of independent modules for tasks such as data and session management, telehealth, daily assistive tasks/actions, together with smart devices and environments, all connected through the framework. After explaining the framework, we illustrate how OpenTera can be used to implement robotic solutions for different applications identified in LTC facilities and homes, and we describe how we plan to validate them through field trials.
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images

This paper presents a client/server privacy-preserving network in the context of multicentric medical image analysis. Our approach is based on adversarial learning which encodes images to obfuscate the patient identity while preserving enough information for a target task. Our novel architecture is composed of three components: 1) an encoder network which removes identity-specific features from input medical images, 2) a discriminator network that attempts to identify the subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case). By simultaneously fooling the discriminator and optimizing the medical analysis network, the encoder learns to remove privacy-specific features while keeping those essentials for the target task. Our approach is illustrated on the problem of segmenting brain MRI from the large-scale Parkinson Progression Marker Initiative (PPMI) dataset. Using longitudinal data from PPMI, we show that the discriminator learns to heavily distort input images while allowing for highly accurate segmentation results. Our results also demonstrate that an encoder trained on the PPMI dataset can be used for segmenting other datasets, without the need for retraining. The code is made available at: https://github.com/bachkimn/Privacy-Net-An-Adversarial-Approach-forIdentity-Obfuscated-Segmentation-of-MedicalImages.
Entropy-Based Watermarking Approach for Sensitive Tamper Detection of Arabic Text

The digital text media is the most common media transferred via the internet for various purposes and is very sensitive to transfer online with the possibility to be tampered illegally by the tampering attacks.Therefore, improving the security and authenticity of the text when it is transferred via the internet has become one of the most dif cult challenges that researchers face today.Arabic text is more sensitive than other languages due to Harakat's existence in Arabic diacritics such as Kasra, and Damma in which making basic changes such as modifying diacritic arrangements can lead to change the text meaning.In this paper, an intelligent hybrid solution is proposed with highly sensitive detection for any tampering on Arabic text exchanged via the internet.Natural language processing, entropy, and watermarking techniques have been integrated into this method to improve the security and reliability of Arabic text without limitations in text nature or size, and type or volumes of tampering attack.The proposed scheme is implemented, simulated, and validated using four standard Arabic datasets of varying lengths under multiple random locations of insertion, reorder, and deletion attacks.The experimental and simulation results prove the accuracy of tampering detection of the proposed scheme against all kinds of tampering attacks.Comparison results show that the proposed approach outperforms all of the other baseline approaches in terms of tampering detection accuracy.
GRAND: A large-scale dataset and benchmark for cervical intraepithelial Neoplasia grading with fine-grained lesion description

Cervical cancer causes the fourth most cancer-related deaths of women worldwide. Early detection of cervical intraepithelial neoplasia (CIN) can significantly increase the survival rate of patients. World Health Organization (WHO) divided the CIN into three grades (CIN1, CIN2 and CIN3). In clinical practice, different CIN grades require different treatments. Although existing studies proposed computer aided diagnosis (CAD) systems for cervical cancer diagnosis, most of them are fail to perform accurate separation between CIN1 and CIN2/3, due to the similar appearances under colposcopy. To boost the accuracy of CAD systems, we construct a colposcopic image dataset for GRAding cervical intraepithelial Neoplasia with fine-grained lesion Description (GRAND). The dataset consists of colposcopic images collected from 8,604 patients along with the pathological reports. Additionally, we invite the experienced colposcopist to annotate two main clues, which are usually adopted for clinical diagnosis of CIN grade, i.e., texture of acetowhite epithelium (TAE) and appearance of blood vessel (ABV). A multi-rater model using the annotated clues is benchmarked for our dataset. The proposed framework contains several sub-networks (raters) to exploit the fine-grained lesion features TAE and ABV, respectively, by contrastive learning and a backbone network to extract the global information from colposcopic images. A comprehensive experiment is conducted on our GRAND dataset. The experimental results demonstrate the benefit of using additional lesion descriptions (TAE and ABV), which increases the CIN grading accuracy by over 10%. Furthermore, we conduct a human-machine confrontation to evaluate the potential of the proposed benchmark framework for clinical applications. Particularly, three colposcopists on different professional levels (intern, in-service and professional) are invited to compete with our benchmark framework by investigating a same extra test set—our framework achieves a comparable CIN grading accuracy to that of a professional colposcopist.
Facial expression recognition using deep learning

Facial expression recognition has become an increasingly important area of research in recent years. Neural network- based methods have made amazing progress in performing recognition-based tasks, winning competitions set up by various data science communities, and achieving high performance on many datasets. Miscellaneous regularization methods have been utilized by various researchers to help combat over-fitting, to reduce training time, and to generalize their models. In this paper, by applying the Haar Cascade classifier to crop faces and focus on the region of interest, we hypothesize that we would attain a fast convergence without using the whole image to analyze facial expressions. We also apply label smoothing and analyze its effect on the databases of CK+, KDEF, and RAF. The ResNet model has been employed as an example of a neural network model. Label smoothing has demonstrated an improvement of the recognition accuracy up to 0.5% considering CK+ and the KDEF databases. While the application of Haar Cascade has shown to decrease the achieved accuracy on KDEF and RAF databases with a small margin, fast convergence of the model has been observed.
Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation

Image-to-image translation is to transfer images from a source domain to a target domain. Conditional Generative Adversarial Networks (GANs) have enabled a variety of applications. Initial GANs typically conclude one single generator for generating a target image. Recently, using multiple generators has shown promising results in various tasks. However, generators in these works are typically of homogeneous architectures. In this paper, we argue that heterogeneous generators are complementary to each other and will benefit the generation of images. By heterogeneous, we mean that generators are of different architectures, focus on diverse positions, and perform over multiple scales. To this end, we build two generators by using a deep U-Net and a shallow residual network, respectively. The former concludes a series of down-sampling and up-sampling layers, which typically have large perception field and great spatial locality. In contrast, the residual network has small perceptual fields and works well in characterizing details, especially textures and local patterns. Afterwards, we use a gated fusion network to combine these two generators for producing a final output. The gated fusion unit automatically induces heterogeneous generators to focus on different positions and complement each other. Finally, we propose a novel approach to integrate multi-level and multi-scale features in the discriminator. This multi-layer integration discriminator encourages generators to produce realistic details from coarse to fine scales. We quantitatively and qualitatively evaluate our model on various benchmark datasets. Experimental results demonstrate that our method significantly improves the quality of transferred images, across a variety of image-to-image translation tasks. We have made our code and results publicly available: http://aiart.live/chan/.
DiTNet: End-to-End 3D Object Detection and Track ID Assignment in Spatio-Temporal World

End-to-end 3D object detection and tracking based on point clouds is receiving more and more attention in many robotics applications, such as autonomous driving. Compared with 2D images, 3D point clouds do not have enough texture information for data association. Thus, we propose an end-to-end point cloud-based network, DiTNet, to directly assign a track ID to each object across the whole sequence, without the data association step. DiTNet is made location-invariant by using relative location and embeddings to learn each object's spatial and temporal features in the Spatio-temporal world. The features from the detection module helps to improve the tracking performance, and the tracking module with final trajectories also helps to refine the detection results. We train and evaluate our network on the CARLA simulation environment and KITTI dataset. Our approach achieves competitive performance over the state-of-the-art methods on the KITTI benchmark.
Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage

This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency.
Fully Unsupervised Person Re-Identification via Selective Contrastive Learning

Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. Existing fully supervised person ReID methods usually suffer from poor generalization capability caused by domain gaps. Unsupervised person ReID has attracted a lot of attention recently, because it works without intensive manual annotation and thus shows great potential in adapting to new conditions. Representation learning plays a critical role in unsupervised person ReID. In this work, we propose a novel selective contrastive learning framework for fully unsupervised feature learning. Specifically, different from traditional contrastive learning strategies, we propose to use multiple positives and adaptively selected negatives for defining the contrastive loss, enabling to learn a feature embedding model with stronger identity discriminative representation. Moreover, we propose to jointly leverage global and local features to construct three dynamic memory banks, among which the global and local ones are used for pairwise similarity computation and the mixture memory bank are used for contrastive loss definition. Experimental results demonstrate the superiority of our method in unsupervised person ReID compared with the state of the art. Our code is available at https://github.com/pangbo1997/Unsup_ReID.git .
Review prognosis system to predict employees job satisfaction using deep neural network

Abstract With the multitude of companies that flourish today, job seekers want to join companies with highly satisfied employees. So, job satisfaction prediction is an important task that helps companies in sustaining or redesigning employee policies. Such predictions not only help in reducing employee attrition but also affect the goodwill and reputation of a company. The higher satisfaction level of current employees attracts potential new employees and confirms the positive policies of a company toward its employees. Job satisfaction prediction can be performed using employee reviews either manually or via automated machine learning algorithms. This study first evaluates four widely used machine learning algorithms, that is, random forest, logistic regression, support vector classifier, and gradient boosting, and then proposes a deep learning model to predict employee job satisfaction level. Experiments are carried out on a dataset that contains text reviews from the employees of Google, Facebook, Amazon, Microsoft, and Apple. Three feature extraction methods are analyzed as well including term frequency‐inverse document frequency (TF‐IDF), bag‐of‐words (BOW), and global vector for word representation (GloVe). Performance is evaluated using accuracy, precision, recall, F1 score, as well as, macro average precision, and weighted average. The performance of the proposed model is compared with state‐of‐the‐art deep learning models. Results demonstrate that the proposed model performs better than both the machine learning and state‐of‐the‐art approaches.
Online variational inference on finite multivariate Beta mixture models for medical applications

Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi-class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria.
A Plaintext-Related Image Encryption Algorithm Based on Compressive Sensing and a Novel Hyperchaotic System

In this paper, an image encryption algorithm based on compressive sensing (CS) and a hyperchaotic system is proposed. This new two-dimensional logical coupling sine chaotic map (2D-LCSCM) has a wider range of chaos, better ergodicity and hyperchaotic properties rather than for existing chaotic maps. By combining with the novel chaotic map and CS, we design an image compression encryption algorithm with good reconstruction accuracy and high security. In the substitution phase of the encryption process, the security performance of the algorithm is improved by the nonlinear transformation of the dynamic substitution box (S-box) and the linear transformation of the chaotic sequences. The performance analyses of the experimental results show that the proposed algorithm can resist various attacks and is highly secure.
Hummer: Text Entry by Gaze and Hum

Text entry by gaze is a useful means of hands-free interaction that is applicable in settings where dictation suffers from poor voice recognition or where spoken words and sentences jeopardize privacy or confidentiality. However, text entry by gaze still shows inferior performance and it quickly exhausts its users. We introduce text entry by gaze and hum as a novel hands-free text entry. We review related literature to converge to word-level text entry by analysis of gaze paths that are temporally constrained by humming. We develop and evaluate two design choices: "HumHum" and "Hummer." The first method requires short hums to indicate the start and end of a word. The second method interprets one continuous humming as an indication of the start and end of a word. In an experiment with 12 participants, Hummer achieved a commendable text entry rate of 20.45 words per minute, and outperformed HumHum and the gaze-only method EyeSwipe in both quantitative and qualitative measures.
Niobium Neuron: RSFQ Based Bio-Inspired Circuit

Neuromorphic and bio-inspired circuits have reached considerable attention since Moore's Law is coming to its limitations. Information processing in mammalian brains takes place in a far more energy-efficient manner and significantly faster than in the best computing architecture nowadays. We propose an approach to bring those benefits to a superconducting information processing circuit. Since the computation in a neuronal network is considered as analogue and the computation as digital, the design is grown around a Josephson comparator with its inherent non-linearity in the transfer function as the central information processing unit. Furthermore, a modified version of the Josephson Transmission Line is used to realize an adaptable coupling between neuron cells. This circuit design benefits of the noise in a 4.2 K environment and is therefore more resilient to noise and switching errors than conventional digital circuits. The proposed circuit behavior in a 2-neuron configuration and the integration in a network topology will be investigated.