Reviewerly/exHarmony huggingface dataset:
A Dense Stereovision System for 3D Body Imaging

Three-dimensional (3D) body imaging has become an important mean for applications based on body measurement, such as apparel customization and obesity assessment. In this article, we present a 3D body imaging system built upon stereovision technology. The system utilizes paired, high-resolution single-lens reflex (SLR) cameras to image the front and back body surfaces of a person, and robust and efficient stereo matching algorithms to reconstruct the 3D surface of the body with high-density data clouds. The system's accuracy and repeatability have been evaluated on mannequins and human subjects in comparison with other measurement methods. It was found that the geometrical measurements from reconstructed 3D body models, including body circumferences and volumes, were highly repeatable and consistent with the manual and other 3D instrumental measurements (CV <; 0.1%, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.99).
Optimizing Software Effort Estimation Models Based On Metaheuristic Methods: A Proposed Framework

Software effort estimation are part of the field of project management in software that is very important for development efforts.Software development planning is something very complex and serious, which determines the success of a software project.Because of the lack of good requirements and information, it causes software project failures.Although there are many studies that aim to solve the problem of noisy, irrelevant and excessive data to achieve accuracy.The purpose of this study is to combine metaheuristic optimization techniques as a framework for using Machine Learning models.By proposing a hybrid estimation model based on a combination of the Satin Bowerbird Optimizer (SBO) algorithm and Support Vector Regression (SVR) to improve the accuracy of software estimation efforts.This study is to determine the effort estimation and duration estimation.The proposed framework is based on theoretical concepts.the proposed model will be tested using a heterogeneous dataset, namely the ISBSG dataset.the results of the study are expected to be used as decision making as the initial planning of software project development.
Phishing detection system using nachine learning classifiers

&lt;span&gt;The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.&lt;/span&gt;
Key-based Reversible Data Masking for Business Intelligence Healthcare Analytics Platforms

Business Intelligence (BI) is quickly becoming a very important tool for all aspects of data analytics. An area that lacks a strong implementation for BI is the healthcare field. BI healthcare analytics platforms facilitate the clinical analysis, financial analysis, supply chain analysis, as well as, fraud and HR analysis. The reason behind the lack of adoption in healthcare arises from the need to meet the legislated and perceived requirements of security and privacy when dealing with clinical information. A strong data masking module is developed based on the key-based reversible approach to protect patients' data privacy, while maintaining the data utility to meet the need for data analytics within BI platforms of the healthcare environment. To ensure the performance of the proposed module, a TPC-H Benchmark analysis is performed which verifies that the analytics results of the masked data are appropriate when compared to the existing masking and encryption methods. The developed module is shown to be secure against the common security threats such as linkage attacks and replay attacks. It uses minimal computational overhead when compared to its counterpart methods and meets the legal requirements to be used safely in the healthcare industry.
Segmentation of Brain Tumor and Performance Evaluation Using Spatial FCM and Level Set Evolution

Background: In recent years, brain tumor is one of the major causes of death in human beings. The survival rate can be increased if the tumor is diagnosed accurately in the early stage. Hence, medical image segmentation is always a challenging task of any problem in computer guided medical procedures in hospitals. The main objective of the segmentation process is to obtain object of interest from the given image so that it can be represented in a meaningful way for further analysis. Methods: To improve the segmentation accuracy, an efficient segmentation method which combines a spatial fuzzy c-means and level sets is proposed in this paper. Results: The experiment is conducted using brain web and DICOM database. After pre-processing of an MR image, a spatial FCM algorithm is applied. The SFCM utilizes spatial data from the neighbourhood of each pixel to represent clusters. Finally, these clusters are segmented using level set active contour model for the tumor boundary. The performance of the proposed algorithm is evaluated using various performance metrics. Conclusion: In this technique, wavelets and spatial FCM are applied before segmenting the brain tumor by level sets. The qualitative results show more accurate detection of tumor boundary and better convergence rate of the contour as compared to other segmentation techniques. The proposed segmentation frame work is also compared with two automatic segmentation techniques developed recently. The quantitative results of the proposed method summarize the improvements in segmentation accuracy, sensitivity and specificity.
The Local Forking Lemma and Its Application to Deterministic Encryption

We bypass impossibility results for the deterministic encryption of public-key-dependent messages, showing that, in this setting, the classical Encrypt-with-Hash scheme provides message-recovery security, across a broad range of message distributions. The proof relies on a new variant of the forking lemma in which the random oracle is reprogrammed on just a single fork point rather than on all points past the fork.
Face Detection with Feature Pyramids and Landmarks

Accurate face detection and facial landmark localization are crucial to any face recognition system. We present a series of three single-stage RCNNs with different sized backbones (MobileNetV2-25, MobileNetV2-100, and ResNet101) and a six-layer feature pyramid trained exclusively on the WIDER FACE dataset. We compare the face detection and landmark accuracies using eight context module architectures, four proposed by previous research and four modified versions. We find no evidence that any of the proposed architectures significantly overperform and postulate that the random initialization of the additional layers is at least of equal importance. To show this we present a model that achieves near state-of-the-art performance on WIDER FACE and also provides high accuracy landmarks with a simple context module. We also present results using MobileNetV2 backbones, which achieve over 90% average precision on the WIDER FACE hard validation set while being able to run in real-time. By comparing to other authors, we show that our models exceed the state-of-the-art for similar-sized RCNNs and match the performance of much heavier networks.
Exploring the Relationship Between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks

Medical image segmentation is an essential tool for clinical decision making and treatment planning. Automation of this process led to significant improvements in diagnostics and patient care, especially after recent breakthroughs that have been triggered by deep learning. However, when integrating automatic tools into patient care, it is crucial to understand their limitations and to have means to assess their confidence for individual cases. Aleatoric and epistemic uncertainties have been subject of recent research. Methods have been developed to calculate these quantities automatically during segmentation inference. However, it is still unclear how much human factors affect these metrics. Varying image quality and different levels of human annotator expertise are an integral part of aleatoric uncertainty. It is unknown how much this variability affects uncertainty in the final segmentation. Thus, in this work we explore potential links between deep network segmentation uncertainties with inter-observer variance and segmentation performance. We show how the area of disagreement between different ground-truth annotators can be developed into model confidence metrics and evaluate them on the LIDC-IDRI dataset, which contains multiple expert annotations for each subject. Our results indicate that a probabilistic 3D U-Net and a 3D U-Net using Monte-Carlo dropout during inference both show a similar correlation between our segmentation uncertainty metrics, segmentation performance and human expert variability.
Bayesian Autoregressive Frailty Models for Inference in Recurrent Events

Abstract We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.
The Adaptation of Shneiderman’s Golden Rules and Nielsen’s Heuristics on Motivational Augmented Reality Technology Design for Young Children

Many research has been found in the development of augmented reality (AR) applications. However, the design guidelines for motivational AR application for young children is still relatively unexplored. This paper discusses how to produce an AR application that can increase the motivation of young children in learning. As a starting point, we reviewed existing design rules from Shneiderman’s Golden Rules and Nielsen’s Ten Heuristics. A set of design guidelines is proposed and will be validated and reported separately. The results of this study will provide AR designers with a set of motivational design guidelines for designing AR technology for young children.
Networking Cooperation in Forming Soft Skills of a New Type of Teacher

The primary aim of this research is to develop an effective network interaction model in developing a new teacher’s soft skills. The authors carried out an analysis of a new teacher’s necessary soft skills and the existing models of partnerships. They outlined their views on the development of a networking model that is aimed at the systematic formation of a teacher’s soft skills. The empirical part is executed based on the Elabuga Institute of the Kazan Federal University. The created model can be used for the organisation of training system of a new type of teacher. It might as well be used as a model of the school-university partnership for revealing pedagogically gifted children or as an integrated model for the formation of basic teachers’ soft skills in the university.
Failure of Normalization in Impredicative Type Theory with Proof-Irrelevant Propositional Equality

Normalization fails in type theory with an impredicative universe of propositions and a proof-irrelevant propositional equality. The counterexample to normalization is adapted from Girard's counterexample against normalization of System F equipped with a decider for type equality. It refutes Werner's normalization conjecture [LMCS 2008].
Interactive Visualization of Saturation Attempts in Vampire

Many applications of formal methods require automated reasoning about system properties, such as system safety and security. To improve the performance of automated reasoning engines, such as SAT/SMT solvers and first-order theorem prover, it is necessary to understand both the successful and failing attempts of these engines towards producing formal certificates, such as logical proofs and/or models. Such an analysis is challenging due to the large number of logical formulas generated during proof/model search. In this paper we focus on saturation-based first-order theorem proving and introduce the SATVIS tool for interactively visualizing saturation-based proof attempts in first-order theorem proving. We build SATVIS on top of the world-leading theorem prover VAMPIRE, by interactively visualizing the saturation attempts of VAMPIRE in SATVIS. Our work combines the automatic layout and visualization of the derivation graph induced by the saturation attempt with interactive transformations and search functionality. As a result, we are able to analyze and debug (failed) proof attempts of VAMPIRE. Thanks to its interactive visualisation, we believe SATVIS helps both experts and non-experts in theorem proving to understand first-order proofs and analyze/refine failing proof attempts of first-order provers.
When DevOps Meets Meta-Learning: A Portfolio to Rule them all

The Machine Learning (ML) world is in constant evolution, as the amount of different algorithms in this context is evolving quickly. Until now, it is the responsibility of data scientists to create ad-hoc ML pipelines for each situation they encounter, gaining knowledge about the adequacy between their context and the chosen pipeline. Considering that it is not possible at a human scale to analyze the exponential number of potential pipelines, picking the right pipeline that combines the proper preprocessing and algorithms is a hard task that requires knowledge and experience. In front of the complexity of building a right ML pipeline, algorithm portfolios aim to drive algorithm selection, learning from the past in a continuous process. However, building a portfolio requires that (i) data scientists develop and test pipelines and (ii) portfolio maintainers ensure the quality of the portfolio and enrich it. The firsts are the developers, while the seconds are the operators. In this paper, we present a set of criteria to be respected, and propose a pipeline-based meta-model, to support a DevOps approach in the context of Machine Learning Pipelines. The exploitation of this meta-model, both as a graph and as a logical expression, serves to ensure continuity between Dev and Ops. We depict our proposition through the simplified study of two primary use cases, one with developer's point-of-view, the other with ops'.
Speeded-Up Robust Feature Matching Algorithm Based on Image Improvement Technology

Due to requirements and necessities in digital image research, image matching is considered as a key, essential and complicating point especially for machine learning.According to its convenience and facility, the most applied algorithm for image feature point extraction and matching is Speeded-Up Robust Feature (SURF).The enhancement for scale invariant feature transform (SIFT) algorithm promotes the effectiveness of the algorithm as well as facilitates the possibility, while the application of the algorithm is being applied in a present time computer vision system.In this research work, the aim of SURF algorithm is to extract image features, and we have incorporated RANSAC algorithm to filter matching points.The images were juxtaposed and asserted experiments utilizing pertinent image improvement methods.The idea based on merging improvement technology through SURF algorithm is put forward to get better quality of feature points matching the efficiency and appropriate image improvement methods are adopted for different feature images which are compared and verified by experiments.Some results have been explained there which are the effects of lighting on the underexposed and overexposed images.
Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments.
Label fusion method combining pixel greyscale probability for brain MR segmentation

Abstract Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods.
Identification of Semantic Patterns in Full-text Documents Using Neural Network Methods

Processing and text mining are becoming increasingly possible thanks to the development of computer technology, as well as the development of artificial intelligence (machine learning). This article describes approaches to the analysis of texts in natural language using methods of morphological, syntactic and semantic analysis. Morphological and syntactic analysis of the text is carried out using the Pullenti system, which allows not only to normalize words, but also to distinguish named entities, their characteristics, and relationships between them. As a result, a semantic network of related named entities is built, such as people, positions, geographical names, business associations, documents, education, dates, etc. The word2vec technology is used to identify semantic patterns in the text based on the joint occurrence of terms. The possibility of joint use of the described technologies is being considered.
Feature Selection Methods for Linked Data: Limitations, Capabilities and Potentials

Feature selection is an important pre-processing, data mining, and knowledge discovery tool for data analysis. By eliminating redundant and irrelevant features from high-dimensional data, feature selection diminishes the 'curse of dimensionality' to improve performance. Data are becoming increasingly complex; heterogeneous data may often be viewed as natural collections of linked objects. Linked data are structured data that are connected with other data sources through the use of semantic queries. It is increasingly prevalent in social media websites and biological networks. Many feature selection methods assume independent and identically distributed data (IID), a condition violated with linked data. In this paper, a review of current feature selection techniques for linked data is presented. Several approaches are examined in various contexts so that performance issues and ongoing challenges can be assessed. The major contribution of this paper is to underscore contemporary uses and limitations of linked data feature selection techniques with the purpose of informing existing capabilities and current potentials for key areas of future research and application.
Non-Committing Encryption with Quasi-Optimal Ciphertext-Rate Based on the DDH Problem

Non-committing encryption (NCE) was introduced by Canetti et al. (STOC ’96). Informally, an encryption scheme is non-committing if it can generate a dummy ciphertext that is indistinguishable from a real one. The dummy ciphertext can be opened to any message later by producing a secret key and an encryption random coin which “explain” the ciphertext as an encryption of the message. Canetti et al. showed that NCE is a central tool to achieve multi-party computation protocols secure in the adaptive setting. An important measure of the efficiently of NCE is the ciphertext rate, that is the ciphertext length divided by the message length, and previous works studying NCE have focused on constructing NCE schemes with better ciphertext rates. We propose an NCE scheme satisfying the ciphertext rate based on the decisional Diffie-Hellman (DDH) problem, where is the security parameter. The proposed construction achieves the best ciphertext rate among existing constructions proposed in the plain model, that is, the model without using common reference strings. Previously to our work, an NCE scheme with the best ciphertext rate based on the DDH problem was the one proposed by Choi et al. (ASIACRYPT ’09) that has ciphertext rate . Our construction of NCE is similar in spirit to that of the recent construction of the trapdoor function proposed by Garg and Hajiabadi (CRYPTO ’18).