Reviewerly/exHarmony huggingface dataset:
Sepsis Prediction Model in the Intensive Care Unit (ICU) Using Support Vector Machine (SVM)

Sepsis is one of life-threatening diseases that is caused by unbalanced body response to some chemicals that the body releases into its blood stream when fighting an infection. Early prediction of sepsis can decrease mortality rates. Machine learning techniques can improve the accuracy of early sepsis prediction. This paper presents an accurate machine learning model that can predict sepsis early. This model depends on support vector machine (SVM) algorithm. This model is applied to the Medical Information Mart for Intensive Care (MIMIC-III). This model obtained an accuracy of 99%.
Saliency Detection via Absorbing Markov Chain with Multi-Level Cues

A robust saliency detection approach for images with a complex background is proposed. The absorbing Markov chain integrating low-level, mid-level and high-level cues dynamically evolves by using the similarity between pixels to detect saliency objects. The experimental results show that the proposed algorithm has advantages in saliency detection, especially for images with a chaotic background or low contrast.
Vision-Based Personal Face Emotional Recognition Approach Using Machine Learning and Tree-Based Classifier

The facial emotion classification is a crucial task in human behavior analysis. By taking static images, the emotion is identified from the face expression. It is one of the categories in image processing that is utilized in a variety of disciplines, including human and computer interaction. Some resources are projected to perform automatic emotion recognition, which utilizes benchmark datasets. This research work is focused on real-time dataset that is used to identify six human facial emotions that are implemented by using SVM and tree-based classifier. Experimental outcome symbolizes the top most presentation on the SVM radial basis function (RBF) kernel recognition (95.49%) when associated to the tree-based classifier.
Optimized Stacking Ensemble Model to Detect Phishing Websites

Phishing attacks are security attacks that do not affect only individuals or organisations websites, but it may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for dispersal hidden junk emails that not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there still a need to enhance the detection accuracy. An optimized ensemble classification method for phishing website detection is proposed in this study. A Genetic Algorithm (GA) was used to optimize the ensemble classification method by tuning the parameters of several ensemble Machine Learning (ML) methods, including Random Forest, AdaBoost, and XGBoost. These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for stacking ensemble method. A phishing website dataset that made up of 4898 phishing websites and 6157 legitimate websites was used for this study experiments. As a result, detection accuracy was enhanced and reached 97.16%.
Together or Not? Exploring Stakeholders in Public and Permissionless Blockchains

The emergence of blockchain projects enables new ways of collaboration between untrusted parties.Each of these projects, however, only exists because stakeholders of these projects find common ground.If this common ground is not found, blockchains are forked -organizationally and technically -which endangered major blockchain systems like Bitcoin or Ethereum.To assure the operation of such projects and, thus, to improve their governance, it is crucial to understand their stakeholders.This research conducted a literature review and a survey to (1) identify blockchain stakeholders and to (2) understand their interests as well as underlying motives for their interests.This research has two main contributions: a stakeholder map, which serves as a lens to study stakeholders of public blockchains, and exemplary insights from the application of this lens comprising of 74 survey responses.Consequently, this research provides a novel tool for stakeholder analysis in academia and practice to improve blockchain governance.
Representation Learning for Short Text Clustering

Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus. Existing pre-trained models (e.g., Word2vec and BERT) have greatly improved the expressiveness for short text representations with more condensed, low-dimensional and continuous features compared to the traditional Bag-of-Words (BoW) model. However, these models are trained for general purposes and thus are suboptimal for the short text clustering task. In this paper, we propose two methods to exploit the unsupervised autoencoder (AE) framework to further tune the short text representations based on these pre-trained text models for optimal clustering performance. In our first method Structural Text Network Graph Autoencoder (STN-GAE), we exploit the structural text information among the corpus by constructing a text network, and then adopt graph convolutional network as encoder to fuse the structural features with the pre-trained text features for text representation learning. In our second method Soft Cluster Assignment Autoencoder (SCA-AE), we adopt an extra soft cluster assignment constraint on the latent space of autoencoder to encourage the learned text representations to be more clustering-friendly. We tested two methods on seven popular short text datasets, and the experimental results show that when only using the pre-trained model for short text clustering, BERT performs better than BoW and Word2vec. However, as long as we further tune the pre-trained representations, the proposed method like SCA-AE can greatly increase the clustering performance, and the accuracy improvement compared to use BERT alone could reach as much as 14%.
Flexible Style Image Super-Resolution Using Conditional Objective

Recent studies have significantly enhanced the performance of single-image super-resolution (SR) using convolutional neural networks (CNNs). While there can be many high-resolution (HR) solutions for a given input, most existing CNN-based methods do not explore alternative solutions during the inference. A typical approach to obtaining alternative SR results is to train multiple SR models with different loss weightings and exploit the combination of these models. Instead of using multiple models, we present a more efficient method to train a single adjustable SR model on various combinations of losses by taking advantage of multi-task learning. Specifically, we optimize an SR model with a conditional objective during training, where the objective is a weighted sum of multiple perceptual losses at different feature levels. The weights vary according to given conditions, and the set of weights is defined as a style controller. Also, we present an architecture appropriate for this training scheme, which is the Residual-in-Residual Dense Block equipped with spatial feature transformation layers. At the inference phase, our trained model can generate locally different outputs conditioned on the style control map. Extensive experiments show that the proposed SR model produces various desirable reconstructions without artifacts and yields comparable quantitative performance to state-of-the-art SR methods. Code and trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/seungho-snu/FxSR</uri> .
Experimental Evaluation of Adder Circuits on IBM QX Hardware

This work experimentally evaluated the performance of quantum adders on various IBM quantum hardware. The authors have constructed quantum circuits for one-qubit and two-qubit quantum adders using Quantum Information Science Kit (Qiskit) and run the circuit on seven IBM quantum devices: YorkTown, Ourense, Valencia, Santiago, Athens, Vigo and Melbourne. A detailed experimental analysis of accuracy rate of seven IBM devices are reported in this work. Experimental analysis shows that IBM Athens (5 qubits) provides the best accuracy results (73.7%) in comparison to high-qubit IBM Melbourne (15 qubits) for one-qubit quantum adder. Experimental analysis shows that IBM Melbourne (15 qubits), the only real IBM quantum hardware presently available with qubits higher than 5 qubits, provides an accuracy of 12.8125% over the ideal simulator.
Improving Question Answering over Knowledge Graphs Using Graph Summarization

Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural language questions using triples contained in a KG. The key idea is to represent questions and entities of a KG as low-dimensional embeddings. Previous KGQAs have attempted to represent entities using Knowledge Graph Embedding (KGE) and Deep Learning (DL) methods. However, KGEs are too shallow to capture the expressive features and DL methods process each triple independently. Recently, Graph Convolutional Network (GCN) has shown to be excellent in providing entity embeddings. However, using GCNs to KGQAs is inefficient because GCNs treat all relations equally when aggregating neighbourhoods. Also, a problem could occur when using previous KGQAs: in most cases, questions often have an uncertain number of answers. To address the above issues, we propose a graph summarization technique using Recurrent Convolutional Neural Network (RCNN) and GCN. The combination of GCN and RCNN ensures that the embeddings are propagated together with the relations relevant to the question, and thus better answers. The proposed graph summarization technique can be used to tackle the issue that KGQAs cannot answer questions with an uncertain number of answers. In this paper, we demonstrated the proposed technique on the most common type of questions, which is single-relation questions. Experiments have demonstrated that the proposed graph summarization technique using RCNN and GCN can provide better results when compared to the GCN. The proposed graph summarization technique significantly improves the recall of actual answers when the questions have an uncertain number of answers.
Combining Deep Learning and ASP-Based Models for the Semantic Segmentation of Medical Images

Automatic segmentation represents a huge breakthrough in computer-aided diagnosis and medicine, as it allows to provide clinicians important with information for interventional and diagnostic tasks. Recent advancements in Deep Learning (DL), such as Convolutional Neural Networks (CNNs), have proved to be greatly promising in identifying anatomical and pathological structures, and in extracting meaningful patterns from huge amounts of data. However, such approaches suffer from the lack of proper means for interpreting the choices made by the models, and it is not easy to drive the decisions according to prior knowledge. In this context, deductive rule-based approaches, such as Answer Set Programming (ASP), can allow to effectively encode problems or specific features via logic programs in a declarative fashion, while possibly also helping at improving performance.In this seminal work, we propose the use of ASP to drive DL approaches in performing semantic segmentation of medical images. Specifically, we encoded prior medical knowledge via ASP, thus defining a rule-based model for deducting all admitted combinations of classes and right locations in medical images. The results of an experimental analysis are reported with the aim to assess the viability of the proposed approach.
Chaos coordinated neural key synchronization for enhancing security of IoT

Abstract The key exchange mechanism in this paper is built utilizing neural network coordination and a hyperchaotic (or chaotic) nonlinear dynamic complex system. This approach is used to send and receive sensitive data between Internet-of-Things (IoT) nodes across a public network. Using phishing, Man-In-The-Middle (MITM), or spoofing attacks, an attacker can easily target sensitive information during the exchange process. Furthermore, minimal research has been made on the exchange of input seed values for creating identical input at both ends of neural networks. The proposed method uses a 5D hyperchaotic or chaotic nonlinear complex structure to ensure the sharing of input seed value across two neural networks, resulting in the identical input on both ends. This study discusses two ways for sharing seed values for neural coordination. The first is a chaotic system with all real variables, whereas the second is a hyperchaotic system with at least one complex variable. Each neural network has its own random weight vector, and the outputs are exchanged. It achieves full coordination in some stages by altering the neuronal weights according to the mutual learning law. The coordinated weights are utilized as a key after the neural coordination technique. The network’s core structure is made up of triple concealed layers. So, determining the inner configuration will be tough for the intruder. The efficiency of the suggested model is validated by simulations, and the findings reveal that the suggested strategy outperforms current equivalent techniques.
Graph Neural Networks in Modern Recommender Systems

Graph is an expressive and powerful data structure that is widely applicable, due to its flexibility and effectiveness in modeling and representing graph structure data. It has been more and more popular in various fields, including biology, finance, transportation, social network, among many others. Recommender system, one of the most successful commercial applications of the artificial intelligence, whose user-item interactions can naturally fit into graph structure data, also receives much attention in applying graph neural networks (GNNs). We first summarize the most recent advancements of GNNs, especially in the recommender systems. Then we share our two case studies, dynamic GNN learning and device-cloud collaborative Learning for GNNs.We finalize with discussions regarding the future directions of GNNs in practice.
Reactive Collision Avoidance for Underactuated Surface Vehicles using the Collision Cone Concept

Avoiding collisions is a crucial task for autonomous systems. Many strategies for avoiding obstacles have been proposed, yet the problem of having underactuated dynamics is rarely addressed in previous studies of collision avoidance algorithms. Underactuation of a system makes the collision avoidance control problem more complex, since the system then lacks the ability to directly control one or more of its degrees of freedom. Therefore, in this paper, we will consider collision avoidance for underactuated vehicles, specifically for the class of vehicles which cannot directly control their sideways speed. This is a broad class, which includes vehicles such as cars, airplanes, and marine vehicles. If the unactuated, sideways velocity component becomes sufficiently large, it can make the vehicle glide sideways rather than moving forward, which in the encounter with an obstacle may be fatal. To tackle this issue, we propose a reactive collision avoidance algorithm, based on the collision cone concept, which is specifically designed to account for the underactuated dynamics of a surface vehicle. We present a rigorous analysis of the closed-loop system and establish explicit conditions guaranteeing vehicle safety. Simulations are included to verify the theoretical result.
Automation of monitoring of drying parameters in hybrid solar-electric dryer for agricultural products

ABSTRACT Increasing the efficiency of solar dryers with ensuring that the system remains accessible to all users can be achieved with their automation through low-cost and easy-to-use technique sensors. The objective was to develop, implement and evaluate an automatic system for monitoring drying parameters in a hybrid solar-electric dryer (HSED). Initially, an automated data acquisition system for collecting the parameters of sample mass, air temperature, and relative air humidity was developed and installed. The automatic mass data acquisition system was calibrated in the hybrid solar-electric dryer. The automated system was validated by comparing it with conventional devices for measuring the parameters under study. The data obtained were subjected to analysis of variance, Tukey test and linear regression at p ≤ 0.05. The system to turn on/off the exhaust worked efficiently, helping to reduce the errors related to the mass measurement. The GERAR Mobile App showed easy to be used since it has intuitive icons and compatibility with the most used operating systems for mobile devices. The responses in communication via Bluetooth were fast. The use of Arduino, a low-cost microcontroller, to automate the monitoring activity allowed estimating the mass of the product and collecting the drying air temperature and relative air humidity data through the DHT22. This sensor showed a good correlation of mass and air temperature readings between the automatic and conventional system, but low correlation for relative air humidity. In general, the automatic data acquisition system monitored in real time the parameters for drying agricultural products in the HSED.
Security Operations Centers: A Holistic View on Problems and Solutions

Since Security Operations Centers (SOCs) were first implemented, they have strived to protect the organization and constituency they serve from all manner of Information Technology (IT) security threats.As SOCs have evolved over time to become as effective and efficient at this as possible, they have struggled with changes and upgrades to their foundational elements of people, processes, and technology in pursuit of this mission.While most relevant literature focuses on one challenge a SOC faces, or one aspect of one problem, the authors of this paper performed a literature review to identify and discuss the top current and future challenges that SOCs face in addition to the top current and future solutions to these problems.
Multi-Trajectory Approach for a Generic Coordination Paradigm of Wheeled Mobile Manipulators

This letter presents a generic coordination approach for the control of mobile manipulators. It is based on an external coupled coordination and exploits a multi-trajectory following approach. A coordination stage provides optimal offsets with respect to system sub-parts reference trajectories that minimize a cost function to manage the system redundancies. In order to face the diversity of tasks to be performed by the robotic platform, the cost function is composed of different criteria that can be adapted, pending on the mission assigned to the robot. A demonstration of its generic properties is proposed on different mobile manipulator structures in simulations. Finally, the effectiveness of the proposed method is evaluated through full-scale experiments with a single-arm skid steering mobile manipulator.
Effect of Fuzzy Time Series Technique on Estimators of Spectral Analysis

In this chapter, we study the estimation of the spectral density function as well as the properties of the resulting estimator, known as the periodogram. The statistical properties of this periodogram for actual and forecasted observations of the fuzzy time series are investigated. The periodogram results in both cases can be compared based on MSEs, which are based on these statistical properties. A programme that converts observed time series to fuzzy time series with large sizes is created for this purpose. Study Objective: Using the fuzzy time series technique to improve the estimation of the spectral density function while retaining its statistical properties. &nbsp
Trustable Co-Label Learning From Multiple Noisy Annotators

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, while it is usually the case that a few trusted samples with clean labels are available. This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators? This paper proposes a data-efficient approach, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trustable Co-label Learning</i> (TCL), to learn deep classifiers from multiple noisy annotators when a small set of trusted data is available. This approach follows the coupled-view learning manner, which jointly learns the data classifier and the label aggregator. It effectively uses trusted data as a guide to generate trustable soft labels (termed co-labels). A co-label learning can then be performed by alternately reannotating the pseudo labels and refining the classifiers. In addition, we further improve TCL for a special complete data case, where each instance is labeled by all annotators and the label aggregator is represented by multilayer neural networks to enhance model capacity. Extensive experiments on synthetic and real datasets clearly demonstrate the effectiveness and robustness of the proposed approach. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShikunLi/TCL</uri> .
Smooth Group L1/2 Regularization for Pruning Convolutional Neural Networks

In this paper, a novel smooth group L1/2 (SGL1/2) regularization method is proposed for pruning hidden nodes of the fully connected layer in convolution neural networks. Usually, the selection of nodes and weights is based on experience, and the convolution filter is symmetric in the convolution neural network. The main contribution of SGL1/2 is to try to approximate the weights to 0 at the group level. Therefore, we will be able to prune the hidden node if the corresponding weights are all close to 0. Furthermore, the feasibility analysis of this new method is carried out under some reasonable assumptions due to the smooth function. The numerical results demonstrate the superiority of the SGL1/2 method with respect to sparsity, without damaging the classification performance.