Reviewerly/exHarmony huggingface dataset:
Efficient Privacy-Preserving Scheme for Location Based Services in VANET System

A Vehicular Ad-hoc Network (VANET) is a type of Mobile Ad-hoc Network (MANET) that is used to provide communications between nearby vehicles, and between vehicles and fixed infrastructure on the roadside.VANET is not only used for road safety and driving comfort but also for infotainment.Communication messages in VANET can be used to locate and track vehicles.Tracking can be beneficial for vehicle navigation using Location Based Services (LBS).However, it can lead to threats on location privacy of vehicle users; since it can profile them and track their physical location.Therefore, to successfully deploy LBS, user's privacy is one of major challenges that must be addressed.In this paper, we propose Privacy-Preserving Fully Homomorphic Encryption over Advanced Encryption Standard (P 2 FHE-AES) scheme for LBS query.This scheme is required for location privacy protection to encourage drivers to use this service without any risk of being pursued.It is implemented using Network Simulator (NS-2), Simulation of Urban Mobility (SUMO), and Cloud simulation (CloudSim).Analysis and evaluation results demonstrate that P 2 FHE-AES scheme can preserve the privacy of the drivers' future routes in an efficient and secure way.The results prove the feasibility and efficiency of P 2 FHE-AES scheme in terms of query's response time, query accuracy, throughput and query overhead.
EagleEye

We present EagleEye, an AR-based system that identifies missing person (or people) in large, crowded urban spaces. Designing EagleEye involves critical technical challenges for both accuracy and latency. Firstly, despite recent advances in Deep Neural Network (DNN)-based face identification, we observe that state-of-the-art models fail to accurately identify Low-Resolution (LR) faces. Accordingly, we design a novel Identity Clarification Network to recover missing details in the LR faces, which enhances true positives by 78% with only 14% false positives. Furthermore, designing EagleEye involves unique challenges compared to recent continuous mobile vision systems in that it requires running a series of complex DNNs multiple times on a high-resolution image. To tackle the challenge, we develop Content-Adaptive Parallel Execution to optimize complex multi-DNN face identification pipeline execution latency using heterogeneous processors on mobile and cloud. Our results show that EagleEye achieves 9.07X faster latency compared to naive execution, with only 108 KBytes of data offloaded.
Improving Classification Performance for a Novel Imbalanced Medical Dataset using SMOTE Method

In recent decades, machine learning algorithms have been used in different fields; one of the most used fields is the health sector.Biomedical data are usually extensive in size, and very hard to be analyzed and interpreted by humans.For this purpose, machine learning models are used to extract hidden patterns from data.In this paper, we aim to analyze, diagnose, and classify diabetes patients using six machine learning algorithms for a new real diabetes dataset.The newly created dataset, called ZADA, is obtained from medical records of about 7000 patients in Zakho city, Kurdistan Region of Iraq.However, our new dataset is imbalanced, which means one class is the minority and the other one is the majority.Class imbalance is a challenging problem in classification, especially in the two-class dataset.When class distributions are imbalanced, traditional machine learning methods often give low classification performance for unseen samples of the minority class.This is because the model tends to be strongly directed by the majority class.To overcome these problems, we first examine the impact of the imbalanced data on the classification performance and hence, use a resampling method to rebalance the data.Furthermore, we utilized three normalization techniques as a preprocessing step to further improve the performance of machine learning algorithms' performance.Therefore, we propose a classification analysis based on the three normalization methods along with the resampling (SMOTE) method to tackle the class imbalance problem.Various experiments are conducted to find the best algorithm with the best performance according to the distribution of minority classes.Results show that the resampling method and the normalization techniques had a positive effect on classification model performance.
Is Economics Research Replicable? Sixty Published Papers From Thirteen Journals Say “Often Not”

We attempt to replicate 67 papers published in 13 well-regarded economics journals using author-provided replication files that include both data and code. Some journals in our sample require data and code replication files, and other journals do not require such files. Aside from 6 papers that use confidential data, we obtain data and code replication files for 29 of 35 papers (83%) that are required to provide such files as a condition of publication, compared to 11 of 26 papers (42%) that are not required to provide data and code replication files. We successfully replicate the key qualitative result of 22 of 67 papers (33%) without contacting the authors. Excluding the 6 papers that use confidential data and the 2 papers that use software we do not possess, we replicate 29 of 59 papers (49%) with assistance from the authors. Because we are able to replicate less than half of the papers in our sample even with help from the authors, we assert that economics research is usually not replicable. We conclude with recommendations on improving replication of economics research. JEL Codes: B41; C80; C82; C87; C88; E01
A Blockchain Based Data Aggregation and Group Authentication Scheme for Electronic Medical System

In recent years, due to the rapid development of information techniques and network technologies, more and more medical documents have been replaced by electronic files for sharing and transmitting in real time. However, medical data transmitted over public communication channels may suffer from security attacks and privacy threats. Blockchain technology has been gotten many attentions in different areas due to its unique properties such as anonymity, verifiability, immutability and decentralization. In order to secure patient privacy and provide more personal healthcare services, in this paper, we propose a data aggregation scheme based on Blockchain technology for medical environments. Moreover, in order to implement remote medical monitoring, we design a group authentication mechanism for multiple authorized users (such as patient, doctors, caregivers, family and friends) to freely access patient's personal health records. The authorized group members in a group will agree on a group session key and use it to protect patient's sensitive information. In case of a new member joins the medical group or an old member leaves the medical group, the group session key needs to be updated at any time. Finally, the electronic medical system will become more secure, reliable and useful by our proposed scheme.
UMLS-based data augmentation for natural language processing of clinical research literature

The study sought to develop and evaluate a knowledge-based data augmentation method to improve the performance of deep learning models for biomedical natural language processing by overcoming training data scarcity.We extended the easy data augmentation (EDA) method for biomedical named entity recognition (NER) by incorporating the Unified Medical Language System (UMLS) knowledge and called this method UMLS-EDA. We designed experiments to systematically evaluate the effect of UMLS-EDA on popular deep learning architectures for both NER and classification. We also compared UMLS-EDA to BERT.UMLS-EDA enables substantial improvement for NER tasks from the original long short-term memory conditional random fields (LSTM-CRF) model (micro-F1 score: +5%, + 17%, and +15%), helps the LSTM-CRF model (micro-F1 score: 0.66) outperform LSTM-CRF with transfer learning by BERT (0.63), and improves the performance of the state-of-the-art sentence classification model. The largest gain on micro-F1 score is 9%, from 0.75 to 0.84, better than classifiers with BERT pretraining (0.82).This study presents a UMLS-based data augmentation method, UMLS-EDA. It is effective at improving deep learning models for both NER and sentence classification, and contributes original insights for designing new, superior deep learning approaches for low-resource biomedical domains.
Style Transfer for Co-speech Gesture Animation: A Multi-speaker Conditional-Mixture Approach

How can we teach robots or virtual assistants to gesture naturally? Can we go further and adapt the gesturing style to follow a specific speaker? Gestures that are naturally timed with corresponding speech during human communication are called co-speech gestures. A key challenge, called gesture style transfer, is to learn a model that generates these gestures for a speaking agent ‘A’ in the gesturing style of a target speaker ‘B’. A secondary goal is to simultaneously learn to generate co-speech gestures for multiple speakers while remembering what is unique about each speaker. We call this challenge style preservation. In this paper, we propose a new model, named Mix-StAGE, which trains a single model for multiple speakers while learning unique style embeddings for each speaker’s gestures in an end-to-end manner. A novelty of Mix-StAGE is to learn a mixture of generative models which allows for conditioning on the unique gesture style of each speaker. As Mix-StAGE disentangles style and content of gestures, gesturing styles for the same input speech can be altered by simply switching the style embeddings. Mix-StAGE also allows for style preservation when learning simultaneously from multiple speakers. We also introduce a new dataset, Pose-Audio-Transcript-Style (PATS), designed to study gesture generation and style transfer. Our proposed Mix-StAGE model significantly outperforms the previous state-of-the-art approach for gesture generation and provides a path towards performing gesture style transfer across multiple speakers. Link to code, data and videos: http://chahuja.com/mix-stage .
Autonomous Driving Trajectory Optimization With Dual-Loop Iterative Anchoring Path Smoothing and Piecewise-Jerk Speed Optimization

This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (PJSO) problem. The work leads to remarkable driving performance improvements including more robust and precise collision avoidance, higher control feasibility, higher computation efficiency and stricter driving comfort guarantee, compared with other existing algorithms. The advantages of our algorithm are attributed to our fast iterative collision checks with exact vehicle/obstacle shapes, strict non-holonomic dynamic constraints and accurate kinematics-based speed optimization. It has been validated that, through batch simulation and road experiments, compared with prior works, our algorithm is with the highest robustness and capable to maintain the lowest failure rate (~7%) at nearly all test conditions, achieves 10x faster computational speed than other planners, fulfills 100% driving-comfort standards in complex driving scenarios, and does not induce significant time increase as boundaries or obstacles scale up.
An Image Compression and Encryption Algorithm Based on the Fractional-Order Simplest Chaotic Circuit

Based on compressive sensing and fractional-order simplest memristive chaotic system, this paper proposes an image compression and encryption scheme. First, a fractional-order simplest memristive chaotic circuit system is designed. The dynamic characteristics of the chaotic system are analyzed by the phase diagram, the Lyapunov exponent's spectrum, and the bifurcation diagram to determine the parameters and pseudo-random sequences used in the encryption scheme. Secondly, an encryption scheme based on compressive sensing is designed. This scheme compresses the image twice to fully reduce the storage cost, and scrambles the pixel matrix twice through block scrambling and zigzag transformation, and then uses chaotic pseudo-random sequence and GF (17) domain diffusion image matrix to obtain the final cipher image. Finally, simulation results and performances analysis indicate that the scheme still has good reconstruction performance, even when the compression ratio is 0.25, and the security analysis shows that it can resist various attacks and has high security.
Measuring the level of cyber-security awareness for cybercrime in Saudi Arabia

<h2>Abstract</h2> The revolution in the Information Technology (IT) field has led to a significant increase in the number of people connected to and utilizing the Internet. However, it has also introduced severe security risks: valuable information such as passwords, financial accounts, and other confidential data are considered attractive targets for attackers. Cyber-attacks against this infrastructure can not only lead to data leakage but can also have significant financial implications and even lead to loss of life. Consequently, to defend against such attacks, and considering that humans have a key role in these technologies, it is important to increase cyber-security awareness. This paper focuses on measuring the current level of cyber-security awareness in Saudi Arabia, in terms of cyber-security practices, level of awareness, and incident reporting, by means of an online questionnaire with 1230 participants. The questionnaire results showed that 31.7% used public Wi-Fi to access the Internet, 51% used their personal information to create their passwords, 32.5% did not have any idea about phishing attacks, 21.7% had been victim of cybercrimes while only 29.2% of them reported the crime, which reflects their levels of awareness. The paper concludes by offering recommendations based on analysis of the results to promote the level of awareness.
Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments

The quantum repeater protocol is a promising approach to implement long-distance quantum communication and large-scale quantum networks. A key idea of the quantum repeater protocol is to use long-lived quantum memories to achieve efficient entanglement connection between different repeater segments with a polynomial scaling. Here we report an experiment which realizes efficient connection of two quantum repeater segments via on-demand entanglement swapping by the use of two atomic quantum memories with storage time of tens of milliseconds. With the memory enhancement, scaling-changing acceleration is demonstrated in the rate for a successful entanglement connection. The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.
The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance

It is recognized that the performance of any prediction model is a function of several factors. One of the most significant factors is the adopted preprocessing techniques. In other words, preprocessing is an essential process to generate an effective and efficient classification model. This paper investigates the impact of the most widely used preprocessing techniques, with respect to numerical features, on the performance of classification algorithms. The effect of combining various normalization techniques and handling missing values strategies is assessed on eighteen benchmark datasets using two well-known classification algorithms and adopting different performance evaluation metrics and statistical significance tests. According to the reported experimental results, the impact of the adopted preprocessing techniques varies from one classification algorithm to another. In addition, a statistically significant difference between the considered data preprocessing techniques is demonstrated.
On Problems Equivalent to (min,+)-Convolution

In recent years, significant progress has been made in explaining the apparent hardness of improving upon the naive solutions for many fundamental polynomially solvable problems. This progress has come in the form of conditional lower bounds—reductions from a problem assumed to be hard. The hard problems include 3SUM, All-Pairs Shortest Path, SAT, Orthogonal Vectors, and others. In the (min ,+)-convolution problem, the goal is to compute a sequence ( c [ i ]) n-1 i=0 , where c [ k ] = min i=0,…; , k { a [ i ] + b [ k - i ]}, given sequences ( a [ i ]) n-1 i=0 and ( b [ i ]) n-1 i=0 . This can easily be done in O( n 2 ) time, but no O ( n 2-ε ) algorithm is known for ε &gt; 0. In this article, we undertake a systematic study of the (min ,+)-convolution problem as a hardness assumption. First, we establish the equivalence of this problem to a group of other problems, including variants of the classic knapsack problem and problems related to subadditive sequences. The (min ,+)-convolution problem has been used as a building block in algorithms for many problems, notably problems in stringology. It has also appeared as an ad hoc hardness assumption. Second, we investigate some of these connections and provide new reductions and other results. We also explain why replacing this assumption with the Strong Exponential Time Hypothesis might not be possible for some problems.
Machine Learning Approaches for Auto Insurance Big Data

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.
Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.
Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers

Abstract In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. We achieved promising improvements when evaluating short as well as long text tasks with the enhancement by our text generation method. Especially with regard to small data analytics, additive accuracy gains of up to 15.53% and 3.56% are achieved within a constructed low data regime, compared to the no augmentation baseline and another data augmentation technique. As the current track of these constructed regimes is not universally applicable, we also show major improvements in several real world low data tasks (up to +4.84 F1-score). Since we are evaluating the method from many perspectives (in total 11 datasets), we also observe situations where the method might not be suitable. We discuss implications and patterns for the successful application of our approach on different types of datasets.
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images

In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.