Reviewerly/exHarmony huggingface dataset:
MR-DARTS: Restricted connectivity differentiable architecture search in multi-path search space

Differentiable search methods can be used to find effective network architectures fast. However, these approaches are accompanied by low accuracy when evaluating a searched architecture, especially evaluating a searched architecture after transferring it to a different dataset. Two reasons can explain this phenomenon. The one is that the networks composed of cells have the depth gap in their structures between the search and evaluate stage. Another is that cells have insufficient ability to extract diverse features. This paper presents the Multi-path Restricted DARTS method to address these critical problems, using a multi-path search space and a restricted connectivity algorithm to perform a more exact search with limited resources. Restricted connectivity algorithm deepens the cells' structure and makes cells more suitable for deep networks to bridge the depth gap. Multi-path search space enables cells to extract and fuse different-scales features to improve the representation capacity of a network. Our approach achieves state-of-the-art performance on CIFAR10 and CIFAR100 with the smallest parameters (only 2.5 M), demonstrating strong transfer learning ability in complex datasets.
Robust Unsupervised Gaze Calibration Using Conversation and Manipulation Attention Priors

Gaze estimation is a difficult task, even for humans. However, as humans, we are good at understanding a situation and exploiting it to guess the expected visual focus of attention of people, and we usually use this information to retrieve people’s gaze. In this article, we propose to leverage such situation-based expectation about people’s visual focus of attention to collect weakly labeled gaze samples and perform person-specific calibration of gaze estimators in an unsupervised and online way. In this context, our contributions are the following: (i) we show how task contextual attention priors can be used to gather reference gaze samples, which is a cumbersome process otherwise; (ii) we propose a robust estimation framework to exploit these weak labels for the estimation of the calibration model parameters; and (iii) we demonstrate the applicability of this approach on two human-human and human-robot interaction settings, namely conversation and manipulation. Experiments on three datasets validate our approach, providing insights on the priors effectiveness and on the impact of different calibration models, particularly the usefulness of taking head pose into account.
Perceptual face inpainting with multicolumn gated convolutional network

Face inpainting is a challenging task in computer vision. Although deep learning-based methods that apply attention mechanism or utilize prior knowledge could reconstruct facial components, they may produce visual artifacts or lack detail texture. To solve mainly these two problems, we propose a multicolumn gated convolutional network (MGCN). MGCN is composed of three parallel branches with gated convolution to dynamically extract multispatial features, which could help to improve the global semantic coherence and achieve more effective performance in irregular mask. Specifically, for generating more plausible texture, we developed a diversified perceptual Markov random field to search correct feature patches in global rather than local images. Experiments on CelebA-HQ face and Flickr-Faces-HQ datasets demonstrate that MGCN achieves a more competitive performance than the state-of-the-art methods.
Evaluating the potential of online review data for augmenting traditional transportation planning performance management

Synchrony of transportation planning with revealed public preferences and expectations has traditionally been difficult to achieve. In other domains, such as manufacturing and design, frameworks have been developed using data-driven analytic processes to extract customer preferences and expectations to inform product development. This study builds from approaches to understanding customer satisfaction in other domains to investigate the potential of social media review mining for bridging the gap between understanding of neighborhood livability and conventional transportation planning decision-making practices. The novelty of the approach is in interpreting an automated user-defined translation of qualitative measures of livability by evaluating users' satisfaction of the neighborhoods through social media and enhancing the traditional approaches to defining livability planning measures (through a sentiment analysis and visualization package). The provided data-driven neighborhood satisfaction package draws the marketing structure of transportation network elements (analogous to the ‘products’ in other domains) with residential nodes as the center of the structure. In this study, the FastText algorithm is tested to employ a social review analysis, common neighborhood aspects are annotated, and neighborhood satisfaction is visualized and compared to the associated demographic side information. Through extracting the important aspects of the public concerns, advanced visualization helps transportation planners better understand the implications for community livability and provides insight into an approach for conducting more equitable public outreach. This approach has the potential to capitalize on residents' interests in social media outlets and to increase public engagement in the planning process by encouraging users to participate in online neighborhood satisfaction reporting. This study demonstrates a proof of concept for a social review analysis and specifies the methodology for agencies to augment planning performance management. (word count: 273).
Ambulance Siren Detection using an MFCC based Support Vector Machine

This paper proposes a mechanism to accurately identify ambulance sirens using an audio signal processing technique and an ML algorithm to provide consistent predictions. The improvement in the accuracy of the machine learning model can be attributed to the improved training dataset. Further, since the training dataset can cause a higher computational overhead, this paper proposes a method to achieve a reduction in the dimensionality of the training dataset. The dataset obtained from the Kaggle platform has been compared with the dataset that is created specifically for this study. This comparative study is used to demonstrate the type of dataset that produces the most efficient prediction of ambulance sirens.
Automatic identification of malaria-infected cells using deep convolutional neural network

Malaria has been a severe ailment spread by the plasmodium parasite, which is conveyed by the bite of an infected Anopheles mosquito. The conventional method of diagnosing malaria is to look for parasite-infected erythrocytes in human blood smears under a microscope by trained personnel. The outcome of this approach depends on the expertise of the persons performing the tests and sometimes they cannot maintain optimal proficiency. Deep learning can be a highly advantageous option as a means of clinical diagnostics. This paper presents two unique deep learning models for identifying malaria from blood cell images based on convolutional neural networks. Accuracy, recall and Matthews correlation coefficient were used to evaluate these models. Proposed method is more effective than the conventional approach of malaria detection which is time-consuming and will help physicians detect the disease more quickly and effortlessly.
YoLite+: a lightweight multi-object detection approach in traffic scenarios

The main challenges of traffic object detection are the density of individual traffic objects and their vulnerability to environmental factors such as weather and light intensity. For the baseline YOLOv4 network with large computation, it is difficult to run efficiently on GPU resource-constrained devices. Based on this question, we propose a lightweight traffic object detection approach, called YoLite+. In this approach, MobileNet and depthwise separable convolution are used to compress the parameters of the baseline YOLOv4 network. Then the approach utilizes the negative phase information of the feature map to improve the model’s accuracy, with almost no increase in the number of parameters. Finally, a data enhancement scheme for multi-scene simulation is proposed to expand the sample diversity and improve the model’s generalization ability. The experimental results show that our approach improves the inference speed by nearly three times over the baseline YOLOv4 and reduces the number of parameters by five times, while the accuracy meets the requirements of the business scenario.
Data visualization: existing tools and techniques

Over time, data have become the essential competitive factor for businesses/enterprises to grow and develop. Selected businesses/enterprises such as information industrial businesses will put more focus on product innovation or technology for solving the challenges of gigantic data, i.e., capture, storage, analysis, and presentation/application. Enterprises/businesses like banking, manufacturing, and other enterprises will also benefit from analysis and management of huge data and provide more prospects for management/strategy/marketing innovations. For centuries, persons/societies have depended on visual illustrations such as maps and charts to grasp information quickly. Due to the way the human brain processes information, it is faster for people to grasp the meaning of many data points when they are displayed in charts and graphs rather than in piles of spreadsheets or long reports. Data visualization is the presentation of data in a graphical or pictorial format. Over time, as data are collected, stored, and analyzed, decision makers at all stages rely on data visualization/presentation software that enables them to see and visually present fruitful analytical results, find significance among the heaps/millions of variables, communicate established concepts and hypotheses to others, and even forecast/predict the future. By exploring each aspect of existing tools and techniques related to data visualization, the major objective of this chapter is to present essential theoretical aspects in an analytical way with a profound focus on challenges to represent data in visual form and limits in terms of pros and cons of existing tools and techniques.
Highly Efficient and Accurate Deep Learning–Based Classification of MRI Contrast on a CPU and GPU

Classifying MR images based on their contrast mechanism can be useful in image segmentation where additional information from different contrast mechanisms can improve intensity-based segmentation and help separate the class distributions. In addition, automated processing of image type can be beneficial in archive management, image retrieval, and staff training. Different clinics and scanners have their own image labeling scheme, resulting in ambiguity when sorting images. Manual sorting of thousands of images would be a laborious task and prone to error. In this work, we used the power of transfer learning to modify pretrained residual convolution neural networks to classify MRI images based on their contrast mechanisms. Training and validation were performed on a total of 5169 images belonging to 10 different classes and from different MRI vendors and field strengths. Time for training and validation was 36 min. Testing was performed on a different data set with 2474 images. Percentage of correctly classified images (accuracy) was 99.76%. (A deeper version of the residual network was trained for 103 min and showed slightly lower accuracy of 99.68%.) In consideration of model deployment in the real world, performance on a single CPU computer was compared with GPU implementation. Highly accurate classification, training, and testing can be achieved without use of a GPU in a relatively short training time, through proper choice of a convolutional neural network and hyperparameters, making it feasible to improve accuracy by repeated training with cumulative training sets. Techniques to improve accuracy further are discussed and demonstrated. Derived heatmaps indicate areas of image used in decision making and correspond well with expert human perception. The methods used can be easily extended to other classification tasks with minimal changes.
Adaptive momentum with discriminative weight for neural network stochastic optimization

Optimization algorithms with momentum have been widely used for building deep learning models because of the fast convergence rate. Momentum helps accelerate Stochastic gradient descent in relevant directions in parameter updating, minifying the oscillations of the parameters update route. The gradient of each step in optimization algorithms with momentum is calculated by a part of the training samples, so there exists stochasticity, which may bring errors to parameter updates. In this case, momentum placing the influence of the last step to the current step with a fixed weight is obviously inaccurate, which propagates the error and hinders the correction of the current step. Besides, such a hyperparameter can be extremely hard to tune in applications as well. In this paper, we introduce a novel optimization algorithm, namely, Discriminative wEight on Adaptive Momentum (DEAM). Instead of assigning the momentum term weight with a fixed hyperparameter, DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value that configures momentum in the current step. In this way, DEAM involves fewer hyperparameters. DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training the deep learning models of both convex and nonconvex situations.
GADO-Net: An Improved AOD-Net Single Image Dehazing Algorithm

With the development of modern industry, the frequency of special weather such as haze has increased, and images taken on foggy days will have blurred picture details, reduced contrast, and partial loss of image information, making subsequent image processing difficult. In previous studies, many scholars have proposed many algorithms with excellent performance to solve such problems, and the more widely used one is AOD-Net. However, there are a series of problems such as easy overfitting of the joint estimation of parameters during training, too small sensory field, and poor defogging effect of low-illumination images. The proposed algorithm&#x2014;GADO-Net uses a depth-separable convolutional neural network instead of a 5-layer convolutional neural network for joint estimation of atmospheric light values and transmittance, while adding a pyramidal pooling module, which makes the algorithm have the advantages of extending the perception field to a certain extent, effectively improving the overfitting phenomenon during training, and improving the ability of the algorithm to fetch global information of foggy sky images. Finally, in this algorithm, the Peak Signal to Noise Ratio (PSNR) is significantly improved by using MS-SSIM and L1 weighting as the LOSS function and using optimization search to obtain the optimal parameters of the model. The experimental show that GAOD-Net performs better than the DCP (Dark Channel Prior), histogram equalization algorithm and conventional AOD-Net in terms of SSE and PSNR, and can dehazing foggy images more effectively.
Proxy-Terms Based Query Obfuscation Technique for Private Web Search

Search engines store users’ queries in a query log for performing personalized information retrieval. However, query logs cause privacy concerns and reveal a lot of information about individuals if used against them. Private web search (PWS) provides a privacy-preserving information retrieval (IR) facility which allows users to retrieve information from an IR system without revealing true search queries. Current PWS techniques that are explored in the domain of web search are query obfuscation-based private web search (OB-PWS). These techniques achieve web privacy by injecting cover queries into the user profiles. However, existing OB-PWS techniques submit true queries along with cover queries and achieve query obfuscation in an isolated manner without considering the similarity between consecutive queries. In this article, we propose a proxy-terms based query obfuscation technique that allows users to retrieve information from an IR system through proxy queries without submitting true queries. IR system automatically generates cover queries and true queries from the proxy queries and cannot differentiate whether the user is trying to retrieve information for the cover queries or true query. We analyzed the effectiveness of the proposed technique on test queries, and develop a similarity metric for testing the accuracy of the proposed technique. Promising results of experiments confirm the effectiveness of the proposed technique.
Graph Substructure Assembling Network with Soft Sequence and Context Attention

There has been a surge of researchers' interest in building predictive models over graphs. However, the overwhelming complexity of graph space often makes it challenging to extract interpretable and discriminative structural features for graph classification. In this work, we propose a new graph neural network model called Substructure Assembling Network (SAN) to learn graph representations for classification. The key innovation is a unified Substructure Assembling Unit (SAU), which is a variant of Recurrent Neural Network (RNN) designed to hierarchically assemble useful pieces of graph components so as to fabricate discriminative substructures. A key challenge is that SAUs need to process the neighbors of a node sequentially while no natural order is defined therein. SAN tries to make the model insensitive to neighborhood orders by randomly shuffling neighborhood sequences in training. However, this could suffer high variance, especially when the neighborhood size is large. Hence, we further propose to equip SAN with a novel module named Soft Sequence with Context Attention (SSCA). SAN-SSCA employs the proposed context attention technique to learn the best "soft" permutation of the neighbors w.r.t. classification. It helps the model achieve higher accuracy as well as lower variance. Experiments confirm the effectiveness of SAN-SSCA.
A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysis on text data.In recent years, dependency parser-based approaches have been commonly studied for opinion target extraction.However, dependency parsers are limited by language and grammatical constraints.Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains.Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement.The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature.The model consists of two stages.In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains.The aspects are also transferred from the source domains to a target domain.Moreover, aspect pruning is applied to further improve the performance of aspect extraction.In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction.The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature.The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.
Bitcoin Price Prediction

Advancement in technological developments introduced virtual currency exchange methods viz Bitcoin, Litecoin, Ethereum and so on which are evolving rapidly. Cryptocurrencies were introduced to eliminate financial intermediaries leading to direct peer-to-peer transactions. With the spread of the global Coronavirus pandemic, the relationship between Bitcoin and the equity market has expanded. Cryptocurrencies are highly volatile but can also prove to be good investments. Cryptocurrency, being a novel technique for transaction systems, has led to a lot of confusion among investors and any rumours or news on social media has been claimed to significantly affect the prices of cryptocurrencies. The huge percentage increase/decrease in Bitcoin's price over a short period of time is an intriguing phenomenon that cannot be foreseen. For a long time, bitcoin price prediction has been a hot topic of study.In this paper, we discuss the implementation and results of the Deep Learning Bitcoin Price Prediction Model and prepare a strategy to maximize gains for investors. The paper covers to framework with a set of deep learning models, analysis methods with a fixed set of factors to predict daily Bitcoin prices and design-integration of price prediction of different cryptocurrencies using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated recurrent units). The idea of incorporating Public Sentiment in the prediction of the hikes and falls of the Bitcoin market from Social Media platforms like Reddit and Twitter leading to meaningful predicted results. This prediction can bring confidence to the common man to invest with lesser risk and more profit. Also, this can enable the digital new-age currency to become a primary method of transaction.
Real-Time Localization and Matching of Corneal Reflections for Eye Gaze Estimation via a Lightweight Network

Eye gaze estimation has lots of potentials in human-computer interaction. A popular scheme for eye gaze estimation in literature uses some infrared (IR) lights to illuminate the eyes while an IR camera captures the images, and the essential step in this scheme is to locate the glints on the cornea illuminated by the IR lights (called corneal reflections) and to match them to the corresponding IR lights. However, corneal reflections are often dim or even absent, and together with other image quality issues, the accuracy and continuity of a gaze estimation system can be severely impaired. Addressing the above problems, this paper designs a new gaze estimation hardware system, and then proposes a lightweight deep neural network for real-time localization and matching of corneal reflections, which can be simply deployed in the designed hardware system. This network, inspired by keypoint detection, can simultaneously locate corneal reflections and match them to the corresponding IR lights. It also merges the two tasks of locating the pupil center and locating the corneal reflections through an attention module. Experiments show the proposed network achieves better performances on localization and matching corneal reflections in comparison to a state-of-the-art method. And additionally, our designed system is able to provide accurate and continuous gaze estimation for real-time applications.