Reviewerly/exHarmony huggingface dataset:
Natural Language Query to SQL Conversion Using Machine Learning Approach

Natural Language Processing is a computer science and artificial intelligence topic concerned with computer-human language interactions and how computers are designed for processing and exploring a variety of natural language data, in particular. The Structured Query Language for non-expert users is usually challenging database storage, they may not know the database structure. For database applications to improve the interaction between database and user, a new intelligent interface is therefore necessary. The concept of utilizing a natural language instead of a structured query language has led to the creation of the natural language interface to database systems as a new form of the processing procedure. This research aims to build a query generating process using an algorithm for machine learning to represent information according to the user's demands for answering the query and obtaining information. For the conversion of Natural Language Query into Structured Query, we utilized a lowercase conversion, removing escaped words, tokenization, PoS tagging, word similarity, Jaro-Winkler matching algorithm, and the method Naive Bayes.
MDE4BBIS: A Framework to Incorporate Model-Driven Engineering in the Development of Blockchain-Based Information Systems

Model-driven engineering is a promising software development methodology that has been investigated in the context of blockchain-based information systems development. Many approaches propose to specify and generate individual components part of such systems&#x2019; architectures using this methodology. In this paper, we provide a high-level overview of the different types of components that can be generated using model-driven engineering in the blockchain context, and of the potential benefits that it could bring in that context. We organize these findings in a framework called MDE4BBIS, which can help identify opportunities to leverage model-driven engineering for different architectural layers in blockchain-based information systems, and promotes an integrated approach.
Deep point cloud landmark localization for fringe projection profilometry

Point clouds have been widely used due to their information being richer than images. Fringe projection profilometry (FPP) is one of the camera-based point cloud acquisition techniques that is being developed as a vision system for robotic surgery. For semi-autonomous robotic suturing, fluorescent fiducials were previously used on a target tissue as suture landmarks. This not only increases system complexity but also imposes safety concerns. To address these problems, we propose a numerical landmark localization algorithm based on a convolutional neural network (CNN) and a conditional random field (CRF). A CNN is applied to regress landmark heatmaps from the four-channel image data generated by the FPP. A CRF leveraging both local and global shape constraints is developed to better tune the landmark coordinates, reject extra landmarks, and recover missing landmarks. The robustness of the proposed method is demonstrated through ex vivo porcine intestine landmark localization experiments.
Fast Locally Optimal Detection of Targeted Universal Adversarial Perturbations

This paper proposes a locally-optimal generalized likelihood ratio test (LO-GLRT) for detecting targeted attacks on a classifier, where the attacks add a norm-bounded targeted universal adversarial perturbation (UAP) to the classifier’s input. The paper includes both an analysis of the test as well as its empirical evaluation. The analysis provides an expression for the approximate lower bound of the detection probability, and the empirical evaluation shows this approximation to be similar to the actual detection probability. Since the LO-GLRT requires the score function of the input distribution, which is usually unknown in practice, we study the LO-GLRT for a learned surrogate input distribution. Specifically, we use a Gaussian distribution over the input subvectors as the surrogate distribution, for its mathematical tractability and computational efficiency. We evaluate the detector for several popular image classifiers and datasets, and compare the statistical and computational performance with the perturbation rectifying network (PRN) detector, another successful approach for detecting the UAPs. The LO-GLRT outperforms the PRN detector on both counts, with a running time at least 100 times lower than that of the PRN detector.
Robust depth estimation on real-world light field images using Gaussian belief propagation

Depth estimation on light field images has been well researched on synthetic datasets. However, existing methods often fail on real-world light field images because of the real-world noises. Due to the lack of real-world light field image datasets for depth estimation, the learning-based methods, e.g., convolutional neural networks, are not capable of performing well on real-world light field images with only synthetic datasets available. In this paper, we adopt the optical flow estimation method to estimate depth from light field images since (i) the existing optical flow methods have shown robustness with significant real-world noises; (ii) the adopted optical flow estimation method does not require any training data, thus not limited to the synthetic datasets. The depth can be solved via the approach of optical flow estimation because (i) a sub-aperture image array can be converted from a light field image; (ii) the optical flow between every two adjacent sub-aperture images of the image array is exactly the disparity between the two images. Furthermore, since a sub-aperture array contains numerous adjacent image pairs, numerous optical flow maps can be generated from the sub-aperture array. We show the depth can be well refined from these numerous optical flow maps through modeling these maps in a single graphical model (a pairwise Gaussian MRF to be precise) and inferencing this graphical model by Gaussian belief propagation (GaBP). Experiments show that i) the proposed method achieves better depth estimation than the state-of-the-art methods on real-world light field images; ii) the proposed method can estimate the depth of objects at a far distance (3.5–9.5 m) much more accurately than the state-of-the-art convolutional neural networks.
DrawingInStyles: Portrait Image Generation and Editing With Spatially Conditioned StyleGAN

The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserve the spatial information under the StyleGAN framework. Specifically, we introduce Spatially Conditioned StyleGAN (SC-StyleGAN for short), which explicitly injects spatial constraints to the original StyleGAN generation process. We explore two input modalities, sketches and semantic maps, which together allow users to express desired generation results more precisely and easily. Based on SC-StyleGAN, we present DrawingInStyles, a novel drawing interface for non-professional users to easily produce high-quality, photo-realistic face images with precise control, either from scratch or editing existing ones. Qualitative and quantitative evaluations show the superior generation ability of our method to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
Unsupervised Domain Adaptation Schemes for Building ASR in Low-Resource Languages

Building an automatic speech recognition (ASR) system from scratch requires a large amount of annotated speech data, which is difficult to collect in many languages. However, there are cases where the low-resource language shares a common acoustic space with a high-resource language having enough annotated data to build an ASR. In such cases, we show that the domain-independent acoustic models learned from the high-resource language through unsupervised domain adaptation (UDA) schemes can enhance the performance of the ASR in the low-resource language. We use the specific example of Hindi in the source domain and Sanskrit in the target domain. We explore two architectures: i) domain adversarial training using gradient reversal layer (GRL) and ii) domain separation networks (DSN). The GRL and DSN architectures give absolute improvements of 6.71% and 7.32%, respectively, in word error rate over the baseline deep neural network model when trained on just 5.5 hours of data in the target domain. We also show that choosing a proper language (Telugu) in the source domain can bring further improvement. The results suggest that UDA schemes can be helpful in the development of ASR systems for low-resource languages, mitigating the hassle of collecting large amounts of annotated speech data.
Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features

Software with natural-language user interfaces has an ever-increasing importance. However, the quality of the included Question Answering (QA) functionality is still not sufficient regarding the number of questions that are answered correctly.In our work, we address the research problem of how the QA quality of a given system can be improved just by evaluating the natural-language input (i.e., the user’s question) and output (i.e., the system’s answer).Our main contribution is an approach capable of identifying wrong answers provided by a QA system. Hence, filtering incorrect answers from a list of answer candidates is leading to a highly improved QA quality. In particular, our approach has shown its potential while removing in many cases the majority of incorrect answers, which increases the QA quality significantly in comparison to the non-filtered output of a system.
Remote research on locomotion interfaces for virtual reality: Replication of a lab-based study on teleporting interfaces

The wide availability of consumer-oriented virtual reality (VR) equipment has enabled researchers to recruit existing VR owners to participate remotely using their own equipment. Yet, there are many differences between lab environments and home environments, as well as differences between participant samples recruited for lab studies and remote studies. This paper replicates a lab-based experiment on VR locomotion interfaces using a remote sample. Participants completed a triangle-completion task (travel two path legs, then point to the path origin) using their own VR equipment in a remote, unsupervised setting. Locomotion was accomplished using two versions of the teleporting interface varying in availability of rotational self-motion cues. The size of the traveled path and the size of the surrounding virtual environment were also manipulated. Results from remote participants largely mirrored lab results, with overall better performance when rotational self-motion cues were available. Some differences also occurred, including a tendency for remote participants to rely less on nearby landmarks, perhaps due to increased competence with using the teleporting interface to update self-location. This replication study provides insight for VR researchers on aspects of lab studies that may or may not replicate remotely.
Wise Feature Selection for Breast Cancer Detection from a Clinical Dataset

Breast cancer is a common cancer, especially in women. Early detection of breast cancer is taken an added importance because it alleviates the rate of mortality and facilitates treatment. Accurate automatic algorithms for the detection of breast cancer are needed. In this paper, we developed a number of different feature selection methods for accurate breast cancer detection. Clinical data was pre-processed and then wise feature selection based on feature importance was applied for feature selection. In addition to this, principal component analysis (PCA), incremental PCA, kernel PCA, independent component analysis, factor analysis, and singular value decomposition methods were implemented and analyzed for feature selection and dimension reduction. Finally, multi-layer perceptron was used for classification. The performance of feature selection methods was evaluated on Breast Cancer Wisconsin Diagnostic dataset with 569 recordings. The best accuracy, sensitivity, specificity, F1-score, and Cohen's kappa on the test data were 97.4%, 98.6%, 95.3%, 97.6%, and 0.94 respectively, with a feature importance method.
When Speaker Recognition Meets Noisy Labels: Optimizations for Front-Ends and Back-Ends

A typical speaker recognition system often involves two modules: a feature extractor front-end and a speaker identification back-end. Despite the superior performance that deep neural networks have achieved for the front-end, their success benefits from the availability of large-scale, correctly labeled datasets. While label noise is unavoidable in speaker recognition datasets, both the front-end and back-end are affected by label noise, which degrades speaker recognition performance. In this paper, we first conduct comprehensive experiments to help improve our understanding of the effects of label noise on both the front-end and back-end. Then, we propose a simple yet effective training paradigm and loss correction method to handle label noise in the front-end. We combine our proposed method with the recently proposed Bayesian estimation of PLDA for noisy labels, and the whole system shows strong robustness to label noise. Furthermore, we show two practical applications of the improved system: one application corrects noisy labels based on an utterance’s chunk-level predictions, and the other algorithmically filters out high-confidence noisy samples within a dataset. By applying the second application to the NIST SRE04–10 dataset and verifying filtered utterances by human validation, we identify that approximately 1% of the NIST SRE04–10 dataset is made up of label errors.
A Target Corner Detection Algorithm Based on the Fusion of FAST and Harris

Corner detection is a common method to obtain image features, and the detection effect influences the performance of matching and tracking directly. A FAST-Harris fusion corner detection algorithm is proposed to improve the shortcomings of the Harris algorithm, such as the low detection accuracy and low positioning accuracy, and a corner detection fusion model is established. First, the detected target image is padded, and then the FAST algorithm is used with a 25% reduced contrast points to achieve fast capture roughly; in this way, a candidate corner set is obtained. Then, screening the candidate corner is set one by one by calculating the response function of the Harris with Scharr operator to achieve capture accurately. Finally, the real corners are obtained using SAD for nonmaximum suppression. The positioning error, error detection rate, robustness, and running time of corner detection are obtained by the PyCharm platform. Compared with Harris, the error detection rate and localization error of the algorithm are reduced by 16.89% and 42.04%, respectively. Compared with 8 popular corner detection algorithms, the error detection rate and localization error of the algorithm in this paper are the lowest, which are 24.60% and 1.42 pixels. The robust performance in lossy JPEG compression is the best, with 17.37% shorter running time than Harris algorithm. The method in this paper can be used in scenarios such as autonomous driving and image search services.
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates

A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold items into an existing catalog. Over the years, a variety of hybrid recommendation models have been proposed to address this problem by utilizing items’ metadata and content along with their ratings or usage patterns. In this work, we wish to revisit the cold start problem in order to draw attention to an overlooked challenge: the ability to integrate and balance between (regular) warm items and completely cold items. In this case, two different challenges arise: (1) preserving high-quality performance on warm items, while (2) learning to promote cold items to relevant users. First, we show that these two objectives are in fact conflicting, and the balance between them depends on the business needs and the application at hand. Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items. We demonstrate the effectiveness of the proposed algorithm on movies, apps, and articles recommendations, and provide an empirical analysis of the cold-warm trade-off.
Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets

Rapid breakthroughs in information technologies have driven substantial developments in artificial intelligence applications, particularly the widespread use of deep learning techniques in domains such as speech, image and text recognition. However, real world data distribution applications suffer from significant problems including data imbalance which can easily lead to machine learning biased towards the side with more data, resulting in inaccurate classification or prediction results. Therefore, effectively addressing data imbalance is a pressing research topic. Generative Adversarial Networks (GAN) addresses data imbalance, but is prone to vanishing gradients. Recent work has thus focused on improving the GAN architecture to resolve this problem. The present research extends these efforts, applying C4.5, Random Forest, Support Vector Machine, K-Nearest Neighbor and Naïve Bayes classification algorithms to a single imbalanced traffic collision dataset to identify methods for improving prediction results. Experimental results show that classification performance significantly improves after data augmentation using Synthetic Minority Oversampling Technique, GAN, Conditional GAN, and Gaussian Discriminant Analysis GAN as compared with the non-augmented dataset. In addition, the Gaussian Discriminant Analysis GAN with Naïve Bayes classifier produces a dataset that optimizes classification performance for traffic accident prediction at highway intersections.
Learning Super-Resolution of Environment Matting of Transparent Objects From a Single Image

This paper addresses the problem of super-resolution of environment matting of transparent objects. In contrast to traditional methods of environment matting of transparent objects, which often require a large number of input images or complex camera setups, recent approaches using convolutional neural networks are more practical. In particular, after training, they can generate the environment mattes using a single image. However, they still do not have super-resolution capabilities. This paper first proposes an encoder-decoder network with restoration units for super-resolution environment matting, called Enhanced Transparent Object Matting Network (ETOM-Net). Then, we introduce a refinement phase to improve the details of the output further. The ETOM-Net effectively recovers lost features in the LR input images and produces visually plausible HR environment mattes and the corresponding reconstructed images, demonstrating our method&#x2019;s effectiveness.
Design and Implementation of Mobile Robot Path Planning Based on A-Star Algorithm

With the rapid development of computer technology, more and more mobile robots appear in people&#x2019;s lives, but they often have the problem of slow search for the optimal path and low work efficiency. In view of the above shortcomings, the path planning of mobile robot is studied in this paper. First, complete the design of the planned path based on the A-star algorithm, and then complete the design and implementation of the drive circuit of the mobile robot&#x2019;s motion unit, including the motor drive circuit and the steering gear drive circuit. The test results show that in a 50*50 grid, and the obstacle coverage rate is 50%, the tracking speed of the A-star algorithm is as high as 0.17s, and the control error of the motor and the steering gear does not exceed 5 %.
HomeID: Home Visitors Recognition using Internet of Things and Deep Learning Algorithms

Nowadays, it is essential to secure your house from unauthenticated persons or thieves. To secure the home, different kind of approaches are considered by researchers. In this paper, securing the house with the help of Internet of Things and image processing techniques. The Proposed system implemented with the help of deep learning algorithms such as MTCNN (Multi-task cascaded convolutional neural network) for face detection and facenet for face recognition. These algorithms will check whenever any person is visiting a house, capture the images of visitors and process those images compared with database images and inform to owner of the house. Then owner can have a eye on those people as well as alert their family members about this.
Application and Development of AI Technology in Automobile Intelligent Cockpit

As Artificial intelligence (AI) shines brightly in various fields., the application of AI technology in the automotive field has become an inevitable trend. Nowadays, more and more people buy cars, and people spend more time in the car cockpit. In order to make users have a more intimate and comfortable experience in the car than before, automakers have introduced AI technology into the cockpit, which is also an important part of the intelligent cockpit. Therefore, this paper summarizes the application status of AI technology in intelligent cockpit, expounds its existing problems, and puts forward corresponding strategies for these problems, which has a certain reference value for the development of AI technology in intelligent cockpit in the future.
Gender-specific Facial Age Group Classification Using Deep Learning

Facial age is one of the prominent features needed to make decisions, such as accessing certain areas or resources, targeted advertising, or more straightforward decisions such as addressing one another.In machine learning, facial age estimation is a typical facial analysis subtask in which a model learns the different facial ageing features from several facial images.Despite several studies confirming a relationship between age and gender, very few studies explored the idea of introducing a gender-based system that consists of two separate models, each trained on a specific gender group.This study attempts to bridge this gap by introducing an age estimation system that consists of two main components.The first component is a custom-built gender classifier that distinguishes females and males apart.The second is an age estimation module that consists of two models.Model A is trained only on female images, while model B is trained only on male images.The system takes an input image, extracts the facial gender then passes the image to the appropriate model based on the predicted gender label.Our age estimation models are based on the Visual Geometry Group (VGG16) networks and have been modified to fit the nature of our problem.The models produce accuracies of more than 85% individually, and the system achieves an overall accuracy of 80%.The proposed system is trained and tested on the UTK-Face dataset and cross-validated on the FG-NET dataset to validate the performance on unseen data.