Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Urdu Tweets Using Markov Chains

This paper presents a sentiment analysis approach based on Markov chains for predicting the sentiment of Urdu tweets. Sentiment analysis has been a focus of natural language processing (NLP) research community from the past few decades. The reason for this growing interest is twofold. First, the complexity involved in identifying sentiment from the unstructured text makes it a challenging problem for the research community. Second, sentiment analysis has a wide variety of applications ranging from industry to academia has made it a popular area in the research field of NLP. However, very little work has been done on sentiment analysis for the low resource languages which include Urdu, Bengali, Hindi, and other Asian languages. This work focuses on developing a 3-class (positive, negative, and neutral) sentiment classification model for the Urdu language. The experiments were conducted on the labeled corpus of Urdu tweets extracted from the Twitter network. One of the main contributions of this research includes the development of a large labeled corpus of Urdu Tweets for sentiment analysis. To the best of our knowledge, there is no such corpus available publicly in the Urdu Language. The labeled dataset is available on GitHub ( https://github.com/zarmeen92/urdutweets ). Furthermore, the results showed that the proposed approach outperforms the lexicon-based and traditional machine learning-based approaches of sentiment analysis.
Accurate Calibration for Large-Scale Tracking-Based Visual Measurement System

The combination of a large-field-of-view visual positioning system and a local scanning sensor affords an effective approach to the 3-D measurement of meter-scale objects and has been actively investigated by researchers. The calibration of such an integrated measurement system is essential to establish the overall measurement accuracy. This article proposes to calculate the transformation relationship between the local system and the transfer target through the determination of the scales of the combined system. The principle and implementation of this tracking-based combined measurement system are described in this article, as well as the use of the respective scales of the combined system to establish a model of the applicable geometrical relationships. The Kronecker product was used to solve the model equations, after which homogenous transformation matrix optimization was performed. An error model of the camera-based measurement system was also established based on a combination of the image distortion and the working volume. The 2-D and 3-D errors of the local and global sensors were further quantitatively simulated and visualized to demonstrate the calibration accuracy. Standard plane and complex large object measurement experiments were performed to verify the accuracy of the proposed measurement system and its use for the reconstruction of a large shape.
Intent-based Control Loop for DASH Video Service Assurance using ML-based Edge QoE Estimation

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.
Multi-level prediction Siamese network for real-time UAV visual tracking

Existing deployed Unmanned Aerial Vehicles (UAVs) visual trackers are usually based on the correlation filter framework. Although these methods have certain advantages of low computational complexity, the tracking performance of small targets and fast motion scenarios is not satisfactory. In this paper, we present a novel multi-level prediction Siamese network (MLPS) for object tracking in UAV videos, which consists of Siamese feature extraction module and multi-level prediction module. The multi-level prediction module can make full use of the characteristics of each layer features to achieve robust evaluation of targets with different scales. Meanwhile, for small-size target tracking, we design a residual feature fusion block, which is used to constrain the low-level feature representation by using high-level abstract semantics, and obtain the improvement of the tracker's ability to distinguish scene details. In addition, we propose a layer attention fusion block which is sensitive to the informative features of each layers to achieve adaptive fusion of different levels of correlation responses by dynamically balancing the multi-layer features. Sufficient experiments on several UAV tracking benchmarks demonstrate that MLPS achieves state-of-the-art performance and runs at a speed over 97 FPS.
Page Segmentation Using Convolutional Neural Network and Graphical Model

Page segmentation of document images remains a challenge due to complex layout and heterogeneous image contents. Existing deep learning based methods usually follow the general semantic segmentation or object detection frameworks, without plentiful exploration of document image characteristics. In this paper, we propose an effective method for page segmentation using convolutional neural network (CNN) and graphical model, where the CNN is powerful for extracting visual features and the graphical model explores the relationship (spatial context) between visual primitives and regions. A page image is represented as a graph whose nodes represent the primitives and edges represent the relationships between neighboring primitives. We consider two types of graphical models: graph attention network (GAT) and conditional random field (CRF). Using a convolutional feature pyramid network (FPN) for feature extraction, its parameters can be estimated jointly with the GAT. The CRF can be used for joint prediction of primitive labels, and combined with the CNN and GAT. Experimental results on the PubLayNet dataset show that our method can extract various page regions with precise boundaries. The comparison of different configurations show that GAT improves the performance when using shallow backbone CNN, but the improvement with deep backbone CNN is not evident, while CRF is always effective to improve, even when combining on top of GAT.
Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

<h2>Abstract</h2><h3>Background</h3> General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward. <h3>Methods</h3> We included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients' emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point. <h3>Results</h3> Of the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83–0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99. <h3>Conclusion</h3> A machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.
Cutting Plane Based Cylinder Fitting Method With Incomplete Point Cloud Data for Digital Fringe Projection

Benefiting from the characteristics of full field scanning, high resolution and high precision, digital fringe projection measurement technology has been widely used in three-dimensional measurement.For the cylinder pose detection, due to the influence of occlusion and noise, the obtained three-dimensional point cloud information is usually incomplete, resulting in the unreliable object parameters after point cloud fitting.In view of the above problems, a cylinder fitting method with incomplete point cloud data based on the cutting plane is proposed.Firstly, the point cloud is processed by bilateral filtering method to reduce the influence of outliers and noise points.Then, according to the principle of forming ellipse on cylinder cutting plane, some equidistant cutting planes are selected.At the same time, in order to reduce the influence of outliers on fitting, the random sampling consistency algorithm is used for ellipse fitting with the points on each cutting plane, that is, the ellipse parameters corresponding to each cutting plane can be obtained.Finally, random sampling consistency algorithm is applied for cylinder axis fitting with the ellipse centers of all the cutting planes, calculating the axis vector of the cylinder.Thus, the parameters of the cylinder detected are obtained.The experimental results of stepped cylinder with diffuse reflection and metal cylinder show the effectiveness and high precision of the proposed method.
Bidirectional Controlled Quantum Teleportation Using Eight-Qubit Quantum Channel in Noisy Environments

In this work, a novel protocol is proposed for bidirectional controlled quantum teleportation (BCQT) in which a quantum channel is used with the eight-qubit entangled state. Using the protocol, two users can teleport an arbitrary entangled state and a pure two-qubit state (QBS) to each other simultaneously under the permission of a third party in the role of controller. This protocol is based on the controlled-not operation, appropriate single-qubit (SIQ) UOs, and SIQ measurements in the Z and X-basis. Also, in this paper, a new criterion of merit named as (predictability of the controller's qubit (QB) by the eavesdropper) is introduced, and the protocol is improved based on it. Then, the proposed protocol is investigated in two typical noisy channels, the amplitude-damping noise (ADN) and the phase-damping noise (PDN). The analysis of the protocol in the noisy environment shows that it only depends on the amplitude of the initial state and the decoherence noisy rate (DR).
Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan Protocols

Objective: The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-Nets have been shown to obtain high segmentation accuracy and appear to generalize well when trained on one scanner type and tested on another scanner, provided that a very similar MR protocol is used. There has, however, been little work addressing the problem of domain adaptation when image intensities or patient orientation differ markedly between the training set and an unseen test set. In this work we aim to address this domain shift problem. Method: We propose to apply extensive intensity augmentation in addition to geometric augmentation during training. We explored both style transfer and a novel intensity remapping approach as intensity augmentation strategies. For our experiments, we trained a 3D U-Net on T1-weighted scans. We tested our network on T2-weighted scans from the same dataset as well as on an additional independent test set acquired with a T1-weighted TWIST sequence and a different coil configuration. Results: By applying intensity augmentation we increased segmentation performance for the T2-weighted scans from a Dice of 0.71 to 0.88. This performance is very close to the baseline performance of training with T2-weighted scans (0.92). On the T1-weighted dataset we obtained a performance increase from 0.77 to 0.85. Conclusion: Our results show that the proposed intensity augmentation increases segmentation performance across different datasets. Significance: The proposed method can improve whole breast segmentation of clinical MR scans acquired with different protocols.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis

We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks. The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network of BSD-GAN, is deliberately split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively "de-freeze" the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Extensive experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image representations and synthesis of novel image contents, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate several image generation and manipulation applications enabled or improved by BSD-GAN.
FEACS: A fog enhanced expressible access control scheme with secure services delegation among carers in E-health systems

The quality of health care delivery is declining with the high cost of health services. This is as a result of the delay in obtaining necessary health information from patients and/or ineffective management of health information. To avert this, a new paradigm called e-health, which fuses wearable medical devices or non-invasive measuring devices with cloud computing has been adopted to improve health care delivery. This allows medical centers and health carers to function more competently and also for patients to obtain better treatments. The adoption of wearable medical devices and cloud computing provides easy and instant access to a large volume of health information. However, it introduces some information management bottlenecks, security, and privacy challenges. In this paper, we propose a security scheme that provides effective health information management and secure access to patients’ health information in an e-health system. The approach also supports cooperative health care delivery among carers through effective information sharing techniques. A two-layer security approach is adopted using the symmetric key and modified ciphertext-policy attribute-based encryption to provide fine-grained access control, time-based attribute revocation, and cooperative health service delegation among carers. Also, fog devices are adopted to create hierarchical storage for effective health information management.
A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.
Protecting the entropic uncertainty lower bound in Markovian and non-Markovian environment via additional qubits

The uncertainty principle is an important principle in quantum theory. Based on this principle, it is impossible to predict the measurement outcomes of two incompatible observables, simultaneously. The uncertainty principle basically is expressed in terms of the standard deviation of the measured observables. In quantum information theory, it is shown that the uncertainty principle can be expressed by Shannon's entropy. The entopic uncertainty lower bound can be altered by considering a particle as the quantum memory which is correlated with the measured particle. We assume that the quantum memory is an open system. We also select the quantum memory from N qubit which interact with common reservoir. In this work we investigate the effects of the number of additional qubits in reservoir on entropic uncertainty lower bound. We conclude that the entropic uncertainty lower bound can be protected from decoherence by increasing the number of additional qubit in reservoir.
Deep neural network approach for arabic community question answering

In this paper, we approach the Arabic community question answering problem. We integrated different types of similarity features, in addition to exploring the effect of using preprocessing. Moreover, we developed a novel deep neural network ensemble model that outperformed the previously achieved performance. Our ensemble model benefits from the semantic and lexical similarity features. Also, our ensemble model uses the recent advances in language models using BERT model. Our proposed model produced a MAP value of (62.9%), an AvgRec value of (86.6%), and an MRR value of (68.86%).
Improving adversarial robustness of deep neural networks by using semantic information

The vulnerability of deep neural networks (DNNs) to adversarial attack, which is an attack that can mislead state-of-the-art classifiers into making an incorrect classification with high confidence by deliberately perturbing the original inputs, raises concerns about the robustness of DNNs to such attacks. Adversarial training, which is the main heuristic method for improving adversarial robustness and the first line of defense against adversarial attacks, requires many sample-by-sample calculations to increase training size and is usually insufficiently strong for an entire network. This paper provides a new perspective on the issue of adversarial robustness, one that shifts the focus from the network as a whole to the critical part of the region close to the decision boundary corresponding to a given class. From this perspective, we propose a method to generate a single but image-agnostic adversarial perturbation that carries the semantic information implying the directions to the fragile parts on the decision boundary and causes inputs to be misclassified as a specified target. We call the adversarial training based on such perturbations "region adversarial training" (RAT), which resembles classical adversarial training but is distinguished in that it reinforces the semantic information missing in the relevant regions. Experimental results on the MNIST and CIFAR-10 datasets show that this approach greatly improves adversarial robustness even using a very small dataset from the training data; moreover, it can defend against FGSM adversarial attacks that have a completely different pattern from the model seen during retraining.
Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Clustering algorithms are designed to deal with the datasets from a wide range of real-world applications based on the task. However, there are some situations where the clusters fail to maintain their performance due to mixed datasets problems. An active research area that is severely affected by these problems is the heart disease dataset. Deep learning methods, the state-of-the-art classifiers, with better learning procedures and computational resources, can fill these gaps. To improve the robustness of clusters, we propose a Constraint-Based Deep Convolutional Generative Adversarial Network (CB-DCGANs) framework for generating simulated data to augment the training set to improve the performance of the clustering algorithm. We evaluated the performance of Discriminatively Boosted Clustering (DBC) in detecting the clusters from given datasets. This study shows that using generative adversarial networks for clustering augmentation can significantly improve performance, especially in real-life applications.
From Hodl to Heist: Analysis of Cyber Security Threats to Bitcoin Exchanges

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.