Reviewerly/exHarmony huggingface dataset:
A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis.Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section.A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients' lives and reduce costs at the earliest possible stage.This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset.The SVM-SMOTE method is used to solve the imbalanced data problem.This paper compares the new model with various machine learning models to validate the efficiency of the introduced model.The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers.Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers.The results of the statistical analysis prove that the proposed model is more effective than other classifiers.Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.
Privacy-Aware Time-Series Data Sharing With Deep Reinforcement Learning

Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we study the privacy-utility trade-off (PUT) in time-series data sharing. Existing approaches to PUT mainly focus on a single data point; however, temporal correlations in time-series data introduce new challenges. Methods that preserve the privacy for the current time may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We consider sharing the distorted version of a user's true data sequence with an untrusted third party. We measure the privacy leakage by the mutual information between the user's true data sequence and shared version. We consider both the instantaneous and average distortion between the two sequences, under a given distortion measure, as the utility loss metric. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL). We evaluate the performance of the proposed solution in location trace privacy on both synthetic and GeoLife GPS trajectory datasets. For the latter, we show the validity of our solution by testing the privacy of the released location trajectory against an adversary network.
Leaky DNN: Stealing Deep-Learning Model Secret with GPU Context-Switching Side-Channel

Machine learning has been attracting strong interests in recent years. Numerous companies have invested great efforts and resources to develop customized deep-learning models, which are their key intellectual properties. In this work, we investigate to what extent the secret of deep-learning models can be inferred by attackers. In particular, we focus on the scenario that a model developer and an adversary share the same GPU when training a Deep Neural Network (DNN) model. We exploit the GPU side-channel based on context-switching penalties. This side-channel allows us to extract the fine-grained structural secret of a DNN model, including its layer composition and hyper-parameters. Leveraging this side-channel, we developed an attack prototype named MosConS, which applies LSTM-based inference models to identify the structural secret. Our evaluation of MosConS shows the structural information can be accurately recovered. Therefore, we believe new defense mechanisms should be developed to protect training against the GPU side-channel.
Breast Cancer Detection Using K-Nearest Neighbors, Logistic Regression and Ensemble Learning

Breast Cancer is one of the most severe diseases that is faced by women leading nowhere other than increased death rates in society and it is considered to be one of the most intense disease in the history of medical science. Looking at the number of deaths caused by Breast cancer, it is considered to be a major threat but today's advancement in medical science has the capability to cure such threat completely if detected at its early stages without causing any harm to the patient. The major challenge arise during the detection of cancer and differentiating between the diagnosis that affirms whether the patient has a benign or malignant type of cancer. Machine Learning Algorithms like K-Nearest Neighbors, Support Vector Machine (SVM) and Artificial Neural Network (ANN) helps us solve this problem by achieving results with high precision and accuracy. The following paper helps in diagnosis of breast cancer using Logistic Regression (LR), K-Nearest Neighbors (KNN) and Ensemble Learning with Principal Component Analysis (PCA) and a comparative study is also made with other papers on the basis of accuracy. The models used here are trained and tested on Wisconsin breast cancer diagnosis data set which is taken from UCI machine learning repository. Pre processing of data was performed followed by feature extraction of data set using Principal Component Analysis (PCA). Various Machine Learning techniques were proposed in the paper which helped us achieve an accuracy of 98.60% using K-Nearest Neighbors, 97.90 using Logistic Regression and 99.30% using Ensemble Learning.
PPGPass: Nonintrusive and Secure Mobile Two-Factor Authentication via Wearables

Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with non-cancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability.
Object-Aware Multimodal Named Entity Recognition in Social Media Posts With Adversarial Learning

Named Entity Recognition (NER) in social media posts is challenging since texts are usually short and contexts are lacking. Most recent works show that visual information can boost the NER performance since images can provide complementary contextual information for texts. However, the image-level features ignore the mapping relations between fine-grained visual objects and textual entities, which results in error detection in entities with different types. To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN). The model jointly extracts entity-related features from both visual objects and texts, and leverages an adversarial training to map two different representations into a shared representation. As a result, domain information contained in an image can be transferred and applied for extracting named entities in the text associated with the image. Experimental results on Tweets dataset demonstrate that our model outperforms the state-of-the-art methods. Moreover, we systematically evaluate the effectiveness of the proposed gated bilinear attention network in capturing the interactions of mutimodal features visual objects and textual words. Our results indicate that the adversarial training can effectively exploit commonalities across heterogeneous data sources, which leads to improved performance in NER when compared to models purely exploiting text data or combining the image-level visual features.
Capturing Human Pose Using mmWave Radar

Human pose estimation is an important task. Traditional human pose capturing systems are based on images or videos, which may suffer from bad light and raise the concerns of privacy. In this paper, we proposed an accurate human pose estimation system using the 77GHz millimeter wave radar. It is the first time that people use off-the-shelf millimeter wave radar to complete such a task. Our system requires no camera or specific sensors on the body to estimate the human skeleton. The system first uses two radar data to generate heatmaps and then adopts CNN to transform two-dimensional heatmaps into human pose. We use coordinated heatmaps from radar and visual inputs extracting from camera together to train the designed network. Based on our dataset and system, the proposed method achieves an average OKS value of 0.705 and 0.877 in AP 50.
SPECMATE: Automated Creation of Test Cases from Acceptance Criteria

In the agile domain, test cases are derived from acceptance criteria to verify the expected system behavior. However, the design of test cases is laborious and has to be done manually due to missing tool support. Existing approaches for automatically deriving tests require semi-formal or even formal notations of acceptance criteria, though informal descriptions are mostly employed in practice. In this paper, we make three contributions: (1) a case study of 961 user stories providing an insight into how user stories are formulated and used in practice, (2) an approach for the automatic extraction of test cases from informal acceptance criteria and (3) a study demonstrating the feasibility of our approach in cooperation with our industry partner. In our study, out of 604 manually created test cases, 56 % can be generated automatically and missing negative test cases are added.
Technical debt forecasting: An empirical study on open-source repositories

Technical debt (TD) is commonly used to indicate additional costs caused by quality compromises that can yield short-term benefits in the software development process, but may negatively affect the long-term quality of software products. Predicting the future value of TD could facilitate decision-making tasks regarding software maintenance and assist developers and project managers in taking proactive actions regarding TD repayment. However, no notable contributions exist in the field of TD forecasting, indicating that it is a scarcely investigated field. To this end, in the present paper, we empirically evaluate the ability of machine learning (ML) methods to model and predict TD evolution. More specifically, an extensive study is conducted, based on a dataset that we constructed by obtaining weekly snapshots of fifteen open source software projects over three years and using two popular static analysis tools to extract software-related metrics that can act as TD predictors. Subsequently, based on the identified TD predictors, a set of TD forecasting models are produced using popular ML algorithms and validated for various forecasting horizons. The results of our analysis indicate that linear Regularization models are able to fit and provide meaningful forecasts of TD evolution for shorter forecasting horizons, while the non-linear Random Forest regression performs better than the linear models for longer forecasting horizons. In most of the cases, the future TD value is captured with a sufficient level of accuracy. These models can be used to facilitate planning for software evolution budget and time allocation. The approach presented in this paper provides a basis for predictive TD analysis, suitable for projects with a relatively long history. To the best of our knowledge, this is the first study that investigates the feasibility of using ML models for forecasting TD.
An intensify atom search optimization for engineering design problems

Atom search optimization is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The main changes of premature convergence and poor balance between exploration and exploitation still persist, which cannot yet do well in solving some complex optimization problems. To solve the above problems and get better performance, an improved atom search optimization with nonlinear inertia weight factor, neighbor learning component and updating mechanisms is proposed in this paper. First, to balance exploration and exploitation better, a nonlinear inertia weight factor is proposed to determine how much the previous velocity and acceleration is preserved. Second, to effectively increase the exchange of information among atoms, a neighbor learning component is integrated into the velocity updating equation, which is beneficial to enhance the exploitation ability. Thirdly, to help atoms jump out from the local optima, an updating mechanism of the best and worst atoms of the current population is proposed, and a greedy selection strategy is employed to boost the probability of finding the optimal solution. Finally, the well-known CEC2017 benchmark functions and four real-world engineering design problems were employed to demonstrate the performance of our algorithm. The experimental results and statistical analysis show that the performance of our algorithm is better than the comparison algorithms.
Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review

The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed.
A Blockchain-Based Trading Platform for Big Data

Nowadays, data is an extremely important asset. Governments around the world encourage big data sharing and trading to flourish the big data economics. However, existing data trading platforms are not fully trusted. Data providers lack control over data trading rights. Meanwhile, the data price model needs further justification. These issues hinder the development of big data trading. In this paper, we propose a blockchain-based decentralized data trading platform, on which data providers can better control data trading. Accordingly, we design smart contracts for distributed data trading and set rules to assign data rewards in trading. The contract guarantees the efficiency of data matching and fair reward distribution. We also adopt proxy re-encryption to ensure the security of data transmission. We verify the security, availability, and efficiency of the proposed big data trading platform.
Multi-modal recognition of worker activity for human-centered intelligent manufacturing

This study aims at sensing and understanding the worker’s activity in a human-centered intelligent manufacturing system. We propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armband and a visual camera are applied to capture Inertial Measurement Unit (IMU) signals and videos, respectively. For the IMU signals, we design two novel feature transform mechanisms, in both frequency and spatial domains, to assemble the captured IMU signals as images, which allow using convolutional neural networks to learn the most discriminative features. Along with the above two modalities, we propose two other modalities for the video data, i.e., at the video frame and video clip levels. Each of the four modalities returns a probability distribution on activity prediction. Then, these probability distributions are fused to output the worker activity classification result. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab a tool/part, hammer a nail, use a power-screwdriver, rest arms, turn a screwdriver, and use a wrench. The developed multi-modal approach is evaluated on this dataset and achieves recognition accuracies as high as 97% and 100% in the leave-one-out and half-half experiments, respectively.
Anomaly Detection in High-Dimensional Data

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation where its k-nearest neighbor distance with the maximum gap is significantly different from what we would expect if the distribution of k-nearest neighbors with the maximum gap is in the maximum domain of attraction of the Gumbel distribution. An approach based on extreme value theory is used for the anomalous threshold calculation. Using various synthetic and real datasets, we demonstrate the wide applicability and usefulness of our algorithm, which we call the stray algorithm. We also demonstrate how this algorithm can assist in detecting anomalies present in other data structures using feature engineering. We show the situations where the stray algorithm outperforms the HDoutliers algorithm both in accuracy and computational time. This framework is implemented in the open source R package stray. Supplementary materials for this article are available online.
An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features

In this paper, a novel approach based on non-inverse matrix online sequence extreme learning machine (NIOSELM) for phishing detection is presented, which takes into account three types of features to comprehensively characterize a website. For the NIOSELM algorithm, we use Sherman Morriso Woodbury equation to avoid the matrix inversion operation, and introduce the idea of online sequence extreme learning machine (OSELM) to update the training model. In order to reduce the dependence of the detection model on the majority class, we use Adaptive Synthetic Sampling (ADASYN) algorithm to generate the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. Furthermore, an improved denoising auto-encoder (SDAE) is designed to reduce the dimension of the experimental dataset. The experimental results show the efficiency and feasibility of the proposed detection mechanism. Moreover, the overall detection performance of NIOSELM is better than that of other existing methods, especially in training speed and the detection accuracy.
Data Augmentation Using Deep Generative Models for Embedding Based Speaker Recognition

Data augmentation is an effective method to improve the robustness of embedding based speaker verification systems, which could be applied to either the front-end speaker embedding extractor or the back-end PLDA. Different from the conventional augmentation methods such as manually adding noise or reverberation to the original audios, in this article, we propose to use deep generative models to directly generate more diverse speaker embeddings, which would be used for robust PLDA training. Conditional GAN, and VAE are designed, and investigated for different embedding types, including factor analysis based i-vector, TDNN based x-vector, and ResNet based r-vector. The proposed back-end augmentation methods are evaluated on NIST SRE 2016, and 2018 dataset. Within the popular x-vector, and r-vector framework, the experimental results show that our proposed methods can outperform the traditional audio based back-end augmentation method while different front-end augmentation methods are considered.
Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold

Integrative multi-view subspace clustering aims to partition observed samples into underlying clusters through fusing representative subspace information from different views into a latent space. The clustering performance relies on the accuracy of sample affinity measurement. However, existing approaches leverage the subspace representation of each view and overlook the learning of appropriate sample affinities. This paper proposes to learn a consensus affinity directly by merging subspace representations of different views on a Grassmann manifold while maintaining their geometric structures across these views. The proposed method not only preserves the structure of the most informative individual view, but also discovers a latent common structure across all views. The associated constrained optimization problem is solved using the alternating direction method of multipliers. Extensive experiments on synthetic and real-world datasets show that the proposed method outperforms several state-of-the-art multi-view subspace clustering methods. The affinity matrix obtained by our method can extract highly representative and latent common information to enhance the clustering performance.