Reviewerly/exHarmony huggingface dataset:
Gender-Specific Predictors of Recidivism Among DUI Offenders

Women are less likely than men to be arrested for driving under the influence (DUI) of alcohol or another drug, yet their proportion of DUI offenders is growing. Understanding how DUI recidivism risk varies for men and women is of practical utility for DUI assessment and intervention programs. The goals of the current study are to determine if there are different sets of predictors for men and women and whether gender-specific DUI recidivism risk scales perform better than a single recidivism scale for both men and women.We rely on statistically driven techniques to develop gender-specific and total sample recidivism risk scales. We then test the ability of the scales to predict recidivism within 12 months among a large sample (N = 10,827, 22.3% female) of DUI offenders court mandated to a DUI intervention in Mississippi.Predictors of recidivism were drawn from measures of criminal history, substance use disorders, driving behaviors, and accidents. Gender-specific models yielded different sets of recidivism risk factors for men and women, with minimal overlap between the two. Male risk factors were criminal history and heavy alcohol consumption. For women, evidence of a substance use disorder was a unique predictor. Having a prior DUI arrest, driving behaviors, and a physical health consequence of alcohol or drug use (i.e., weight loss) were shared predictors for both sexes.Findings suggest that within broad categories of risk factors, the predictive validity of specific assessment items may vary by sex. Our methods represent progression toward more efficient prediction of DUI recidivists.
Path Planning for Mobile Robot using Dubins-curve based RRT Algorithm with Differential Constraints

This paper treats a path planning problem for the mobile robot with differential constraints using modified RRT (Rapidly exploring random tree) algorithm based on Dubin's curves. In this paper, the planning problem is considered as a problem of finding a feasible path between the initial and goal point in a static environment with obstacles. Modifications on basic RRT algorithm are necessary due to differential constraints of non-holonomic car-like robot. Dubin's vehicle is selected as robot model, which represents special case of Simple car that moves only forward with constant velocity and can only make left and right turns. Algorithm is implemented in ROS (Robot Operating System) and tested through realistic simulations in 2D environment consisted of free space and obstacles. The simulation setup was conducted using a 2D Stage simulator in ROS and RViz tool for visualization. Pioneer 3AT robot model was used as Dubin's car for simulation purposes.
Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery

Abstract Medical augmented reality (AR) is an increasingly important topic in many medical fields. AR enables x-ray vision to see through real world objects. In medicine, this offers pre-, intra- or post-interventional visualization of “hidden” structures. In contrast to a classical monitor view, AR applications provide visualization not only on but also in relation to the patient. However, research and development of medical AR applications is challenging, because of unique patient-specific anatomies and pathologies. Working with several patients during the development for weeks or even months is not feasible. One alternative are commercial patient phantoms, which are very expensive. Hence, this data set provides a unique collection of head and neck cancer patient PET-CT scans with corresponding 3D models, provided as stereolitography (STL) files. The 3D models are optimized for effective 3D printing at low cost. This data can be used in the development and evaluation of AR applications for head and neck surgery.
Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.
Efficient Verifiable Key-Aggregate Keyword Searchable Encryption for Data Sharing in Outsourcing Storage

In a secure data sharing system, users can selectively retrieve encrypted files by performing keyword search over the ciphertext of data.Most of the existing searchable encryption schemes can provide security protection for both data owner and users.Nevertheless, three pivotal issues need to be addressed.Firstly, the cloud might return a wrong result or incomplete result for some reasons, e.g., saving the computing resources.Secondly, users need to store massive keys to generate trapdoors and decrypt the ciphertext of data, which brings great challenges to users' key management.Thirdly, when users perform keyword search over a large number of files, they need to generate and submit massive trapdoors, which is unrealistic.Proceeding from these points, in this paper, we propose an efficient verifiable key-aggregate keyword searchable encryption (EVKAKSE) scheme.In this scheme, the data owner distributes only one aggregate key to users for keyword search, decryption and verification, who can use the aggregate key to generate a single trapdoor for keyword search over shared files.Generally, we define the requirements of the scheme, analyze the threat models and give a valid construction.Furthermore, our security analysis and experimental evaluation demonstrate that the scheme is efficient and secure. INDEX TERMS Searchable encryption, cloud storage
Quintuple Local Coordinate Images for Local Shape Description

Owing to poor descriptiveness, weak robustness, and high computation complexity of local shape descriptors (&lt;small&gt;LSDs&lt;/small&gt;), point-cloud registration in the case of partial overlap and object recognition in a cluttered environment are still challeng- ing tasks. For this purpose, an &lt;small&gt;LSD&lt;/small&gt; is developed in this article by proposing a new local reference frame (&lt;small&gt;LRF&lt;/small&gt;) method and designing a novel feature representation. In the &lt;small&gt;LRF&lt;/small&gt; method, two weighting methods are applied to obtain robustness to noise, point-density variation, and incomplete shape. Additionally, a vector representation is calculated to disambiguate the sign of the x-axis. The feature representation encodes the local information by generating the local coordinate images from five views. Thus, more geometric and spatial information is included in the descriptor. Finally, the performance of the &lt;small&gt;LRF&lt;/small&gt; method and the &lt;small&gt;LSD&lt;/small&gt; is evaluated on several popular data sets. The experimental results demonstrate well that the &lt;small&gt;LRF&lt;/small&gt; is robust to noise, point-density variation, and incomplete shape, and the &lt;small&gt;LSD&lt;/small&gt; holds strong robustness, superior descriptiveness, and high computational efficiency.
LAMBERT: Layout-Aware language Modeling using BERT for information extraction.

In this paper we introduce a novel approach to the problem of understanding documents where the local semantics is influenced by non-trivial layout. Namely, we modify the Transformer architecture in a way that allows it to use the graphical features defined by the layout, without the need to re-learn the language semantics from scratch, thanks to starting the training process from a model pretrained on classical language modeling tasks.
Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis

Imbalanced training data introduce important challenge into medical image analysis where a majority of the data belongs to a normal class and only few samples belong to abnormal classes. We propose to mitigate the class imbalance problem by introducing two generative adversarial network (GAN) architectures for class minority oversampling. Here, we explore balancing data distribution 1) by generating new sample from unsupervised GAN or 2) synthesize missing image modalities from semi-supervised GAN. We evaluated the effect of the synthetic unsupervised and semi-supervised GAN methods by use of 1,500 MR images for brain disease diagnosis, where the classification performance of a residual network was compared between unbalanced datasets, classic data augmentation, and the proposed new GAN-based methods.The evaluation results showed that the synthesized minority samples generated by GAN improved classification accuracy up to 18% in term of Dice score.
Designing a Performance Certification Test for Automatic Detection Equipment based on Artificial Intelligence Technology

Purpose: This paper examined the design of a standard test method for certifying the performance of automatic detection equipment using artificial intelligence (AI).BRMethods: First, indicators for measuring the detection performance were determined and the terms were redefined. Then, the number of iterations required to give a criterion value at a given confidence level for data following the binomial distribution was calculated. As a case study, an aviation security x-ray scanner was tested.BRResults: If failures occur before the number of allowable times during the trial, the minimum probability of occurrence of true positive (TP) can be demonstrated. This means that F2-Score is satisfied, and the performance can be certified.BRConclusion: This method can be used in industries that operate automatic detectors based on AI technology. The method should contribute to the development of a national certification system and improve product quality control.
Applying the ETL Process to Blockchain Data. Prospect and Findings

We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction. We describe the implementation of address clustering algorithms specific for the Bitcoin blockchain and the process to collect and transform data and to load them in the database. To balance the input data rate with the elaboration time, we manage blockchain data according to the lambda architecture. To evaluate our process, we first analyzed the performances in terms of scalability, and then we checked its usability by analyzing loaded data. Finally, we present the results of a toy analysis, which provides some findings about blockchain data, focusing on a comparison between the statistics of the last year of transactions, and previous results of historical blockchain data found in the literature. The ETL process we realized to analyze blockchain data is proven to be able to perform a reliable and scalable data acquisition process, whose result makes stored data available for further analysis and business.
Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.
On the Development of Context-Aware Augmented Reality Applications

Augmented Reality (AR) is a technique that enables users to interact with their physical environment through the overlay of digital information. With the spread of AR applications in various domains (e.g. product design, manufacturing or maintenance) and the introduction of concepts such as Pervasive Augmented Reality (PAR), the aspect context-awareness started to play an important role. By sensing the user's current context and adapting the AR application accordingly, an adequate user experience can be achieved. Due to the complex structure and composition of AR applications, their development is a challenging task. Although, context-awareness for AR systems was addressed to some extent, a systematic method for development of context-aware AR applications is not fully covered yet. Therefore, in this paper, we identify the main challenges for development of context-aware AR applications and sketch our solution idea for a model-based development framework. The benefit and applicability of our solution idea is shown on the basis of two example mobile AR applications supporting context-awareness.
Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem

The imbalanced classification problem has become greatest issue in many fields, especially in fraud detection.In fraud detection, the transaction datasets available for training are extremely imbalanced, with fraudulent transaction logs considerably less represented.Meanwhile, the feature information of the fraud samples with better classification capabilities cannot be mined directly by feature learning methods due to too few fraud samples.These significantly reduce the effectiveness of fraud detection models.In this paper, we proposed a Dual Autoencoders Generative Adversarial Network, which can balance the majority and minority classes and learn feature representations of normal and fraudulent transactions to improve the accuracy of the fraud detection.The new model firstly trains a Generative Adversarial Networks to output sufficient mimicked fraudulent transactions for autoencoder training.Then, two autoencoders are trained on the normal and fraud dataset, respectively.The samples are encoded by two autoencoders to obtain two sets of features, which are combined to form the dual autoencoding features.Finally, the model detects fraudulent transactions by a Neural Network trained on the augmented training set.Experimental results show that the model outperforms a set of well-known classification methods in experiments, especially the sensitivity and precision, which are effectively improved.
Multi-Scale Attention Generative Adversarial Networks for Video Frame Interpolation

Video frame interpolation is a fundamental task in computer vision.Recent methods usually apply convolutional neural networks to generate intermediate frame with two consecutive frames as inputs.But sometimes existing methods fail to handle with complex motion and long-range dependencies.In this paper, a multi-scale dense attention generative adversarial network is proposed.First, a multi-scale generative adversarial framework is established for video frame interpolation.Generators from coarse to fine can better combine global and local information.Second, an attention module introduced to generator makes network accurately focus on moving objects.Third, a sequence discriminator is designed to improve the ability of capturing spatial and temporal consistency in frame sequence.Experimental results of ablation study prove the effectiveness of our three contributions.And results on several datasets demonstrate that our approach attains higher performance and produce more photo-realistic in-between frame comparing with previous works.
3D face reconstruction from mugshots: Application to arbitrary view face recognition

Mugshots while routinely acquired by law enforcement agencies are under utilized by automated face recognition systems. In this paper, we propose a regression based approach to reconstruct textured full 3D face models from multi-view mugshot images. Using landmarks from the input frontal and profile mugshots of a subject, our method reconstructs his⧹her 3D face shape via either linear or nonlinear regressors. The texture of the mugshot images is mapped to the reconstructed 3D face shape via an efficient seamless texture recovery scheme. Compared with existing 3D face reconstruction methods, the proposed method more effectively utilizes the three-view mugshot face images collected during booking. The reconstructed 3D faces are used to generate realistic multi-view face images to enlarge the gallery and facilitate arbitrary view face recognition. Evaluation experiments have been done on BFM and Bosphorus databases in terms of reconstruction accuracy, and on Multi-PIE and Color FERET databases in terms of recognition accuracy. The results show that the proposed method can reduce the 3D face reconstruction error of the best competitive method from 2.31 mm to 1.88 mm, and improve the recognition accuracy of state-of-the-art deep learning based face matchers by as much as ~4% on Multi-PIE and ~2% on Color FERET despite the high baseline set by them.
On the Feistel Counterpart of the Boomerang Connectivity Table

At Eurocrypt 2018, Cid et al. introduced the Boomerang Connectivity Table (BCT), a tool to compute the probability of the middle round of a boomerang distinguisher from the description of the cipher’s Sbox(es). Their new table and the following works led to a refined understanding of boomerangs, and resulted in a series of improved attacks. Still, these works only addressed the case of Substitution Permutation Networks, and completely left out the case of ciphers following a Feistel construction. In this article, we address this lack by introducing the FBCT, the Feistel counterpart of the BCT. We show that the coefficient at row Δi, ∇o corresponds to the number of times the second order derivative at points Δi, ∇o) cancels out. We explore the properties of the FBCT and compare it to what is known on the BCT. Taking matters further, we show how to compute the probability of a boomerang switch over multiple rounds with a generic formula.
Using Mobile Technologies to Enhance Accessibility and Inclusion in Field-Based Learning

The relevance of field education in the geosciences has been subject to increasing scrutiny, in part due to the exclusionary nature of traditional field practices that require independent work and physical agility.As an alternative, this article presents strategies for increasing accessibility and inclusion in collaborative field-based education through the use of mobile technologies.We present a series of examples to show how the use of mobile technologies in the field can enable collaborative observation, data collection, data sharing, and interpretation.The strategies developed in these examples provide equitable access to instruction, peer engagement, and participation in every field exercise.We suggest that technological approaches to accessibility and inclusion in the field can facilitate opportunities for all students to gain field experiences that are an important component of geoscience education.
A benchmark dataset and case study for Chinese medical question intent classification

Abstract Background To provide satisfying answers, medical QA system has to understand the intentions of the users’ questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Chinese medical intent classification, and the datasets of other fields are not applicable to the medical QA system. To solve this problem, we construct a Chinese medical intent dataset (CMID) using the questions from medical QA websites. On this basis, we compare four intent classification models on CMID using a case study. Methods The questions in CMID are obtained from several medical QA websites. The intent annotation standard is developed by the medical experts, which includes four types and 36 subtypes of users’ intents. Besides the intent label, CMID also provides two types of additional information, including word segmentation and named entity. We use the crowdsourcing way to annotate the intent information for each Chinese medical question. Word segmentation and named entities are obtained using the Jieba and a well-trained Lattice-LSTM model. We loaded a Chinese medical dictionary consisting of 530,000 for word segmentation to obtain a more accurate result. We also select four popular deep learning-based models and compare their performances of intent classification on CMID. Results The final CMID contains 12,000 Chinese medical questions and is organized in JSON format. Each question is labeled the intention, word segmentation, and named entity information. The information about question length, number of entities, and are also detailed analyzed. Among Fast Text, TextCNN, TextRNN, and TextGCN, Fast Text and TextCNN models have achieved the best results in four types and 36 subtypes intent classification, respectively. Conclusions In this work, we provide a dataset for Chinese medical intent classification, which can be used in medical QA and related fields. We performed an intent classification task on the CMID. In addition, we also did some analysis on the content of the dataset.