Reviewerly/exHarmony huggingface dataset:
Bi-Convex Approximation of Non-Holonomic Trajectory Optimization

Autonomous cars and fixed-wing aerial vehicles have the so-called non-holonomic kinematics which non-linearly maps control input to states. As a result, trajectory optimization with such a motion model becomes highly non-linear and non-convex. In this paper, we improve the computational tractability of non-holonomic trajectory optimization by reformulating it in terms of a set of bi-convex cost and constraint functions along with a non-linear penalty. The bi-convex part acts as a relaxation for the non-holonomic trajectory optimization while the residual of the penalty dictates how well its output obeys the non-holonomic behavior. We adopt an alternating minimization approach for solving the reformulated problem and show that it naturally leads to the replacement of the challenging non-linear penalty with a globally valid convex surrogate. Along with the common cost functions modeling goal-reaching, trajectory smoothness, etc., the proposed optimizer can also accommodate a class of non-linear costs for modeling goal-sets, while retaining the bi-convex structure. We benchmark the proposed optimizer against off-the-shelf solvers implementing sequential quadratic programming and interior-point methods and show that it produces solutions with similar or better cost as the former while significantly outperforming the latter. Furthermore, as compared to both off-the-shelf solvers, the proposed optimizer achieves more than 20x reduction in computation time.
INTERNET OF THINGS EXPERT SYSTEM FOR SMART CITIES USING THE BLOCKCHAIN TECHNOLOGY

Blockchain technology apparently is a trivial innovation, but this technology has attracted huge investors in a very short period compared to other technologies, and it is still having a lot of potential applications. Smart contracts are making possible execution in an automated and safe way by using blockchain technology. Therefore, smart contracts are applied in this research for the expert system. This paper is about an expert system working with smart contracts and neural networks as the inference machine to decide on the sensors optimal distribution and taking actions when sensor readings are out of range: control lights, activating fire alarms, temperature alarms, etc. for all spaces (parks, schools, hospitals, etc.) in a smart city based on the needs, and likes of the expert system user. This expert system works using a blockchain structure on the EOSIO ecosystem with all data gathered by the sensors being saved in cloud online making internet of things environment and essential data saved in a blockchain node.
Face Recognition Under Low Illumination Via Deep Feature Reconstruction Network

Recent major benchmarks show that deep-learning-based face recognition can achieve superb performance, even surpassing human capability. However, many state-of-the-art face recognition models suffer from severe performance degradation for images captured under low illumination. The issue can be addressed by enhancing the illumination of face images before performing face recognition. In this paper, we evaluate such enhancement methods and, based on the findings, propose a novel feature reconstruction network to make face features illumination-invariant by generating a feature image from both the raw face image and the illumination-enhanced face image. The performance of the proposed approach is tested on the Specs on Faces (SoF) dataset. The overall verification accuracy is improved by 0.5% to 2.5% and the rank-l identification accuracy is improved by 2.1%.
Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation

Deep learning strategies have become ubiquitous optimization tools for medical image analysis. With the appropriate amount of data, these approaches outperform classic methodologies in a variety of image processing tasks. However, rare diseases and pediatric imaging often lack extensive data. Specially, MRI are uncommon because they require sedation in young children. Moreover, the lack of standardization in MRI protocols introduces a strong variability between different datasets. In this paper, we present a general deep learning architecture for MRI homogenization that also provides the segmentation map of an anatomical region of interest. Homogenization is achieved using an unsupervised architecture based on variational autoencoder with cycle generative adversarial networks, which learns a common space (i.e. a representation of the optimal imaging protocol) using an unpaired image-to-image translation network. The segmentation is simultaneously generated by a supervised learning strategy. We evaluated our method segmenting the challenging anterior visual pathway using three brain T1-weighted MRI datasets (variable protocols and vendors). Our method significantly outperformed a non-homogenized multi-protocol U-Net.
Binarized Neural Architecture Search for Efficient Object Recognition

Traditional neural architecture search (NAS) has a significant impact in computer vision by automatically designing network architectures for various tasks. In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing. The BNAS calculation is more challenging than NAS due to the learning inefficiency caused by optimization requirements and the huge architecture space, and the performance loss when handling the wild data in various computing applications. To address these issues, we introduce operation space reduction and channel sampling into BNAS to significantly reduce the cost of searching. This is accomplished through a performance-based strategy that is robust to wild data, which is further used to abandon less potential operations. Furthermore, we introduce the upper confidence bound to solve 1-bit BNAS. Two optimization methods for binarized neural networks are used to validate the effectiveness of our BNAS. Extensive experiments demonstrate that the proposed BNAS achieves a comparable performance to NAS on both CIFAR and ImageNet databases. An accuracy of 96.53% vs. 97.22% is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a 40% faster search than the state-of-the-art PC-DARTS. On the wild face recognition task, our binarized models achieve a performance similar to their corresponding full-precision models.
TimeSAN: A Time-Modulated Self-Attentive Network for Next Point-of-Interest Recommendation

Next Point-of-Interest (POI) recommendation aims to rank a list of POIs by their attractiveness to users based on the users' historical records of POI visits. This task is challenging, because user preferences may be influenced by various contextual factors. In this paper, we consider the temporal contextual factor, i.e., the time of users' POI visits. Previous attempts for modelling the impact of temporal contexts can be categorized into two groups: factorization based methods and recurrent neural network based methods. The first group adds a time dimension to their latent recommendation spaces, which may suffer from the data sparsity problem due to the additional dimension. The second group uses time-aware contextual gates to update the hidden and cell states in RNNs, which may have limited capability in capturing long-range temporal dynamics. In this paper, we propose a time -modulated s elf-attentive network (TimeSAN) for next POI recommendation. This model learns the relevance between a user's next POI visit and her historical visits via the self-attention mechanism, where the relevance is modulated by the temporal contextual influence. The learned time-aware relevance is further fused with users' long-term interests to provide final recommendations. We conduct extensive experiments on real-world datasets. The results confirm that TimeSAN outperforms previous methods consistently and significantly in recommendation accuracy, while attaining a high model training efficiency.
Cooperative API misuse detection using correction rules

Application Programming Interfaces (APIs) grant developers access to the functionalities of code libraries. Due to missing knowledge of how an API is correctly used, developers can unintentionally misuse APIs, and thus introduce bugs. To tackle this issue, recent techniques aim to automatically infer specifications for correct API usage and detect misuses. Unfortunately, these techniques suffer from high false-positive rates, leading to many false alarms. While we believe that existing techniques will improve in the future, in this paper, we propose to investigate a different route: We assume that a developer manually detected and fixed an API misuse relating to a third-party library. Based on the change, we can infer a correction rule for the API misuse. Then, we can use this correction rule to detect the same and similar API misuses in the same or other projects. This represents a cooperative technique to transfer the knowledge of API-misuse fixes to other developers. We report promising insights on an implementation and empirical evidence on the applicability of our technique based on 43 real-world API misuses.
A Model Based on Program Slice and Deep Learning for Software Defect Prediction

Defects are inherent in software and can lead to many serious problems during the use of software. Software defect prediction is an important method for finding defects and can help developers improve their testing efficiency. To build accurate prediction models, previous software defect prediction techniques focus on design of functions related to the code of potential defects. But these methods do not adequately capture the semantic features of the program. In this paper, we propose a software defect prediction model based on program slice and deep learning. We extract program slice based on system dependence graph and leverage Gated Recurrent Unit (GRU) to generate features. We conducted experiments both within-project defect prediction and cross-project defect prediction using the dataset of the public PROMISE database. The results show that our method improves on average by 11.0% in F1-measure in within-project and 10.4% in F1-measure in cross-project defect prediction compared to the Tree-LSTM method.
Cross-Project Vulnerability Prediction Based on Software Metrics and Deep Learning

Vulnerability prediction constitutes a mechanism that enables the identification and mitigation of software vulnerabilities early enough in the development cycle, improving the security of software products, which is an important quality attribute according to ISO/IEC 25010. Although existing vulnerability prediction models have demonstrated sufficient accuracy in predicting the occurrence of vulnerabilities in the software projects with which they have been trained, they have failed to demonstrate sufficient accuracy in cross-project prediction. To this end, in the present paper we investigate whether the adoption of deep learning along with software metrics may lead to more accurate cross-project vulnerability prediction. For this purpose, several machine learning (including deep learning) models are constructed, evaluated, and compared based on a dataset of popular real-world PHP software applications. Feature selection is also applied with the purpose to examine whether it has an impact on cross-project prediction. The results of our analysis indicate that the adoption of software metrics and deep learning may result in vulnerability prediction models with sufficient performance in cross-project vulnerability prediction. Another interesting conclusion is that the performance of the models in cross-project prediction is enhanced when the projects exhibit similar characteristics with respect to their software metrics.
Blockchain for supply chain performance and logistics management

Abstract The trend of digitalization uses modern relationship models across global supply chain (SC) management. The main objective of this article is to identify SC current problems with the help of the knowledge sharing aspect using blockchain technology. This article addresses selected problems for managing modern SCs with a view to shared knowledge based on information management, effects of product and service on the quality of information, effects of consumer demands occurrences on information flow, outsourcing and globalization, and emerging threats to SCs by concerned parties. These problems are classified as concerns of confidentiality and trust, which are unable to share information among SC stakeholders due to a lack of data standards. To address these problems, a hybridized probabilistic performance and logistics framework (HPP‐LF) has been developed that defines the validity of blockchain to help companies throughout the design and deployment of potential blockchain solutions to address the information sharing issues between SC stakeholders. The proposed HPP‐LF results are compared with the existing PMOS, MIP, UTAUT, and ILOG techniques. The experimental result shows that the proposed method has better performance when compared with other existing systems.
A Robust Handwritten Digit Recognition System Based on Sliding window with Edit distance

Handwritten Number or digit recognition plays an important role in different platforms and applications. Here, a novel technique based on a string edit distance algorithm is proposed to recognize offline handwritten digit images. The recognition system tries to build up each digit's string and then predicts the digit class by comparing the test string against the existing string from the training set. The lower the edit distance among the two digit's string, the greater the chances of similarity among them. Some digit databases have evaluated this system and a variety of mixed datasets have been developed to validate the system's robustness. So that a dataset can train the system and another dataset can test it. Four types of datasets are used as evidence for the excellence of the robustness of the projected system. In some cases, the recognition system is trained with very less training samples from a specific dataset, but a large number of samples from a different dataset are tested. The outcomes of these experiments are shown by tabular as well as a pictographic version.
Bayesian approach to longitudinal craniofacial growth: The Craniofacial Growth Consortium Study

Abstract Early in the 20th century, a series of studies were initiated across North America to investigate and characterize childhood growth. The Craniofacial Growth Consortium Study (CGCS) combines craniofacial records from six of those growth studies (15,407 lateral cephalograms from 1,913 individuals; 956 females, 957 males, primarily European descent). Standard cephalometric points collected from the six studies in the CGCS allows direct comparison of craniofacial growth patterns across six North American locations. Three assessors collected all cephalometric points and the coordinates were averaged for each point. Twelve measures were calculated from the averaged coordinates. We implemented a multilevel double logistic equation to estimate growth trajectories fitting each trait separately by sex. Using Bayesian inference, we fit three models for each trait with different random effects structures to compare differences in growth patterns among studies. The models successfully identified important growth milestones (e.g., age at peak growth velocity, age at cessation of growth) for most traits. In a small number of cases, these milestones could not be determined due to truncated age ranges for some studies and slow, steady growth in some measurements. Results demonstrate great similarity among the six growth studies regarding craniofacial growth milestone estimates and the overall shape of the growth curve. These similarities suggest minor variation among studies resulting from differences in protocol, sample, or possible geographic variation. The analyses presented support combining the studies into the CGCS without substantial concerns of bias. The CGCS, therefore, provides an unparalleled opportunity to examine craniofacial growth from childhood into adulthood.
Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending

Data imbalance is a major problem that affects several machine learning (ML) algorithms. Such a problem is troublesome because most of the ML algorithms attempt to optimize a loss function that does not take into account the data imbalance. Accordingly, the ML algorithm simply generates a trivial model that is biased toward predicting the most frequent class in the training data. In the case of histopathologic images (HIs), both low-level and high-level data augmentation (DA) techniques still present performance issues when applied in the presence of inter-patient variability; whence the model tends to learn color representations, which is related to the staining process. In this paper, we propose a novel approach capable of not only augmenting HI dataset but also distributing the inter-patient variability by means of image blending using the Gaussian-Laplacian pyramid. The proposed approach consists of finding the Gaussian pyramids of two images of different patients and finding the Laplacian pyramids thereof. Afterwards, the left-half side and the right-half side of different HIs are joined in each level of the Laplacian pyramid, and from the joint pyramids, the original image is reconstructed. This composition combines the stain variation of two patients, avoiding that color differences mislead the learning process. Experimental results on the BreakHis dataset have shown promising gains vis-à-vis the majority of DA techniques presented in the literature.
IoT Digital Forensics Readiness in the Edge: A Roadmap for Acquiring Digital Evidences from Intelligent Smart Applications

Entering the era of the Internet of Things, the traditional Computer Forensics is no longer as trivial as decades ago with a rather limited pool of possible computer components. It has been demonstrated recently how the complexity and advancement of IoT are being used by malicious actors attack digital and physical infrastructures and systems. The investigative methodology, therefore, faces multiple challenges related to the fact that billions of interconnected devices generate tiny pieces of data that easily comprehend the Big Data paradigm. As a result, Computer Forensics is no longer a simple methodology of the straightforward process. In this paper, we study the complexity and readiness of community-accepted devices in a smart application towards assistance in criminal investigations. In particular, we present a clear methodology and involved tools related to Smart Applications. Relevant artefacts are discussed and analysed using the prism of the Digital Forensics Process. This research contributes towards increased awareness of the IoT Forensics in the Edge, corresponding challenges and opportunities.
Sample Complexity of Probabilistic Roadmaps via ε-nets

We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time (non-asymptotic) regime. In particular, we investigate how completeness and optimality guarantees of the approach are influenced by the underlying deterministic sampling distribution X and connection radius r > 0. We develop the notion of (δ, ε)-completeness of the parameters X, r, which indicates that for every motion-planning problem of clearance at least δ > 0, PRM using X, r returns a solution no longer than 1+ε times the shortest δ-clear path. Leveraging the concept of e-nets, we characterize in terms of lower and upper bounds the number of samples needed to guarantee (δ, ε)-completeness. This is in contrast with previous work which mostly considered the asymptotic regime in which the number of samples tends to infinity. In practice, we propose a sampling distribution inspired by e-nets that achieves nearly the same coverage as grids while using fewer samples.
Shear-zone-related gold mineralization in quartz-carbonate veins from metamafic rocks of the BIF-hosted world-class Cuiabá deposit, Rio das Velhas greenstone belt, Quadrilátero Ferrífero, Brazil: Vein classification and structural control

The Cuiabá deposit, located in the Quadrilátero Ferríferro district (QF) in the southeastern part of the Minas Gerais state, is the largest underground mine in Brazil (5.78 Moz resources of 10.26 g/t Au − 1.18 Moz of ore reserves and 6 Moz of overall production). This world-class orogenic gold deposit is part of the Rio das Velhas greenstone belt, a typical association of mafic volcanic rocks, banded iron formation (BIF), carbonaceous and micaceous phyllites, metamorphosed to the greenschist facies conditions. Historically, economic gold production at Cuiabá derives from BIF-hosted, replacement-style sulfide (pyrite and pyrrhotite) ores. More recently, economically significant shear-zone-related gold mineralization in quartz-carbonate veins systems, hosted in metamafic rocks, were identified. They are associated with distal (chlorite-), intermediate (carbonate-) and proximal (sericite-dominant) hydrothermal alteration zones. Structures related to the gold-bearing quartz-carbonate veins were generated during a single, progressive ductile to brittle deformational event (D1), with the compositional banding Sb and the flexural foliation Sf created in the early stages of deformation. As deformation progressed, shear zones, isoclinal folds, the mylonitic foliation Sm, the mineral lineation Lmin and the intersection lineation Lint were formed simultaneously to the main stage of hydrothermal fluid input. Flanking folds and flanking shear bands represent the late stage of deformation and the final hydrothermal fluid pulse. Four quartz-carbonate vein systems are identified: V1, V2, V3 and V4. The V1 mineralized system is an Sm-controlled fault-fill type, associated with the principal, gold-bearing fluid input stage. The V2 veins are also mineralized, controlled by the Sf foliation and subdivided in three sub-types, according to internal crystal orientation: V2a extensional, V2b oblique-extensional and V2c, a fault-fill vein. The extensional array V3 veins are also controlled by the Sm foliation, whereas the extensional, breccia-style V4 system has veins associated with flanking structures. The structural analysis of veins indicates a shortening vector rotation during D1 accompanied by hydrothermal fluid input. Since all vein systems are related to the D1 event, each one defining an incremental deformational stage, they provide information about the hydrothermal fluid evolution at Cuiabá. A proposed evolutionary model for the vein systems suggest that the ore-forming fluid responsible for gold mineralization was channeled along shear zones and dispersed out into the Sb and Sf structures. The internal vein features and the trapping temperatures of quartz from each vein system indicate a progressively decrease in tectonic activity.