Reviewerly/exHarmony huggingface dataset:
Staged Sketch-to-Image Synthesis via Semi-supervised Generative Adversarial Networks

Sketch-based image synthesis is a challenging problem in computer graphics and vision. Existing approaches either require exact edge maps or rely on the retrieval of existing photographs, which limits their applications in real-world scenarios. Accordingly in this work, we propose a staged semi-supervised generative adversarial networks based method for sketch-to-image synthesis, which can directly generate realistic images from novice sketches. More specifically, we first adopt a conditional generative adversarial network (CGAN) to extract class-wise representations from unpaired images. These class-wise representations are then exploited and incorporated with another CGAN, which are used to generate realistic images from sketches. By incorporating the class-wise representations, our method can leverage both the general class information from unpaired images and the targeted object information from input sketches. Additionally, this network architecture also enables us to take full advantage of widely available unpaired images and learn more accurate class representations. Extensive experiments demonstrate, compared with state-of-the-art image translation methods, our approach can achieve more promising results and synthesize images with significantly better Inception Scores and Fréchet Inception Distance.
Randomized non-linear PCA networks

PCANet is an unsupervised Convolutional Neural Network (CNN), which uses Principal Component Analysis (PCA) to learn convolutional filters. One drawback of PCANet is that linear PCA cannot capture nonlinear structures within data. To address this problem, a straightforward approach is utilizing kernel methods by equipping the PCA method in PCANet with a kernel function. However, this practice leads to a network having cubic complexity with respect to the number of training image patches. In this paper, we propose a network called Randomized Nonlinear PCANet (RNPCANet), which uses an explicit kernel PCA to learn the convolutional filters. Although RNPCANet utilizes kernel methods for nonlinear processing of data, using kernel approximation techniques to define an explicit feature space in each stage, we theoretically show that the complexity of this model is not much higher than that of PCANet. We also show that our method links PCANets to Convolutional Kernel Networks (CKNs) as the proposed model maps the patches to a kernel feature space similar to CKNs. We evaluate our model on image recognition tasks including Coil-20, Coil-100, ETH-80, Caltech-101, MNIST, and C-Cube datasets. The experimental results show that the proposed method has superiority over PCANet and CKNs in terms of recognition accuracy.
Estimating People Flows to Better Count Them in Crowded Scenes

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it also enables us to exploit the correlation between people flow and optical flow to further improve the results. We will demonstrate that we consistently outperform state-of-the-art methods on five benchmark datasets.
A hybrid fuzzy feature selection algorithm for high-dimensional regression problems: An mRMR-based framework

One of the most important factors affecting the interpretability of Fuzzy Rule-Based Systems (FRBSs) is the number of features used Indeed, the employment of a large number of features could be problematic for the components of an FRBS. In these cases,feature selection approaches can be employed to discover the most beneficial features. In this study, we present HFFS a Hybrid Fuzzy Feature Selection algorithm for high-dimensional regression problems. It benefits from both filter and wrapper methods. HFFS is composed of two main components: a filter-based Selector and a wrapper-based Modifier. Selector is an mRMR-based framework that sequentially selects the most informative features and adds them to a candidate subset. However, since these features may not result in an efficient FRBS, they are evaluated by Modifier, which fine-tunes them whenever their overall performance declines. This procedure is repeated until no further progress is possible within the candidate subset, acting as the stopping point for the algorithm. The effectiveness of HFFS was evaluated using twenty-eight real-world regression datasets and four different types of FRBSs. The experimental results and statistical tests confirmed that HFFS was able to improve the estimation accuracy compared to the filter methods and reduce the computation times compared to the wrapper methods.
Deep Unsupervised Binary Descriptor Learning Through Locality Consistency and Self Distinctiveness

Deep learning has been successfully applied to learn local feature descriptors in recent years. However, most of existing methods are supervised methods relying on a large number of labeled training patches, which are also proposed for learning real valued descriptors. In this paper, we propose a novel unsupervised deep learning method for binary descriptor learning. The binary descriptors are much more compact and efficient than the real valued descriptors and unsupervised leaning is highly required in many applications due to its label-free characteristic as the annotations are sometimes expensive to obtain. The core idea of our method is to explore the locality consistency in the descriptor space as well as to distinguish different patches while maintaining the ability to match a patch with its geometric transformed ones. We also give a theorical analysis about the role of batch normalization in learning effective binary descriptors. Benefited from this analysis, there is no need to append two additional losses on minimizing the quantization error and maximizing the entropy to the final learning objective like previous works did, thus simplifying our network training. Experiments on four benchmarks demonstrate that the proposed method is able to learn binary descriptors significantly outperforming previous unsupervised binary descriptors, even superior to most supervised ones. Especially, it obtains 21.2% of improvement on the UBC Phototour dataset, and 19.8%, 26.7%, 26.0% of improvements for patch verification, matching, retrieval tasks respectively on the HPatches dataset compared to the previous best unsupervised method.
Consentio: Managing Consent to Data Access using Permissioned Blockchains

The increasing amount of personal data is raising serious issues in the context of privacy, security, and data ownership. Entities whose data are being collected can benefit from mechanisms to manage the parties that can access their data and to audit who has accessed their data. Consent management systems address these issues. We present Consentio, a scalable consent management system based on the Hyperledger Fabric permissioned blockchain. The challenge we address is to ensure high throughput and low latency of endorsing data access requests and granting or revoking consent. Experimental results show that our system can handle as many as 6,000 access requests per second, allowing it to scale to very large deployments.
Low-Depth Mechanisms for Quantum Optimization

One of the major application areas of interest for both near-term and fault-tolerant quantum computers is the optimization of classical objective functions. In this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations. We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This physical language, in combination with uniqueness results related to unitarity, allow us to identify some potential pitfalls from kinetic energy fundamentally opposing the goal of optimization. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution. As an example, we explore the surprising deficiency of many quantum methods in solving uncoupled spin problems and how this is both predictive of performance on some more complex systems while immediately suggesting simple resolutions. Further examination of canonical problems like the Hamming ramp or bush of implications show that entanglement can be strictly detrimental to performance results from the underlying mechanism of solution in approaches like QAOA. Kinetic energy and graph Laplacian perspectives provide new insights to common initialization and optimal solutions in QAOA as well as new methods for more effective layerwise training. Connections to classical methods of continuous extensions, homotopy methods, and iterated rounding suggest new directions for research in quantum optimization. Throughout, we unveil many pitfalls and mechanisms in quantum optimization using a physical perspective, which aim to spur the development of novel quantum optimization algorithms and refinements.
StyleGuide: Zero-Shot Sketch-Based Image Retrieval Using Style-Guided Image Generation

The goal of zero-shot sketch-based image retrieval is to retrieve relevant images from a search set against a hand-drawn sketch query, which belongs to a class, previously unseen by the model. The knowledge gap between such unseen and seen classes along with the domain-gap between the query and search-set makes the problem extremely challenging. In this work, we address this problem by proposing a novel retrieval methodology, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StyleGuide</i> using style-guided <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake</i> -image generation. In addition, we further study the scenario of generalized zero-shot sketch-based image retrieval, where the search set contains images from both seen and unseen categories. Specifically, we propose a detection approach for unseen class samples in the search-set, based on pre-computed seen class-prototypes, to obtain a refined search-set for a particular unseen-class query. Thus, the query sketch needs to be compared only to those image data which are more likely to belong to the unseen classes, resulting in improved retrieval performance. Extensive experiments on two large-scale sketch-image datasets, Sketchy extended and TU-Berlin show that the proposed approach performs better or comparable to the state-of-the-art for ZS-SBIR and gives significant improvements over the state-of-the-art for generalized ZS-SBIR.
A Hybrid Bayesian Ridge Regression-CWT-Catboost Model For PV Power Forecasting

The forecasting of the high intermittency of Photovoltaic (PV) energy in smart grid is a persisting challenge. The proposed paper takes this challenge by presenting accurate forecasting techniques. PV power forecasting contributes to energy sector stability, controllability, and utilization through systematic monitoring for proper energy operation and optimization of grid-load balance. This paper addresses a novel paradigm that effectively copes with unpredictable extreme meteorological conditions. The proposed technique combines the Bayesian Ridge Regression (BRR) model, Continuous Wavelet Transform (CWT), and Gradient boosting with categorical features (Catboost). The architecture of the proposed model is based on the acquisition of features inputs, which sorts those features according to their importance. This ranking deploys a Bayesian Ridge Regression model to select the most relevant features. Then, the CWT decomposition technique converts the features chosen into a time-frequency domain. Catboost model generates the forecast output for one day ahead. The final results are deduced using inverse CWT. The Australian weather data have been used to evaluate the performance of the proposed technique on short short-term power forecasting for large-scale PV plants. The evaluation has been conducted using score metrics, visualization curves, and to-fold cross-validation. Simulation results are conducted to confirm the performance of the proposed technique.
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion

Abstract Generalizability is a concern when applying a deep learning (DL) model trained on one dataset to other datasets. It is challenging to demonstrate a DL model’s generalizability efficiently and sufficiently before implementing the model in clinical practice. Training a universal model that works anywhere, anytime, for anybody is unrealistic. In this work, we demonstrate the generalizability problem, then explore potential solutions based on transfer learning by using the cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion task as the testbed. Previous works only studied on one or two anatomical sites and used images from the same vendor’s scanners. Here, we investigated how a model trained for one machine and one anatomical site works on other machines and other anatomical sites. We trained a model on CBCT images acquired from one vendor’s scanners for head and neck cancer patients and applied it to images from another vendor’s scanners and for prostate, pancreatic, and cervical cancer patients. We found that generalizability could be a significant problem for this particular application when applying a trained DL model to datasets from another vendor’s scanners. We then explored three practical solutions based on transfer learning to solve this generalization problem: the target model, which is trained on a target dataset from scratch; the combined model, which is trained on both source and target datasets from scratch; and the adapted model, which fine-tunes the trained source model to a target dataset. We found that when there are sufficient data in the target dataset, all three models can achieve good performance. When the target dataset is limited, the adapted model works the best, which indicates that using the fine-tuning strategy to adapt the trained model to an unseen target dataset is a viable and easy way to implement DL models in the clinic.
Web Personalization Model Using Modified S3VM Algorithm For developing Recommendation Process

the problem is frequently named as “multidimensional data (raw data set)” which discusses the difficult and drawbacks of processing and evaluating huge amounts of data. This is considered helpful in variety of domains such as education, online newspaper, medicine, and financial businesses having huge collections of raw data that are warehoused. Our research goal was to propose a novel intelligent information personalization model for user preference recommendation model based on S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> VM concept. To applying the proposed model for online newspaper information classification and recommendation improvement. Our research goal to increase performance of user behavior prediction and information classification using a data fusion level Semi supervise supports Vector Machines, compare the technique with the traditional machine learning algorithm and proposed a novel technique.
HAR-Depth: A Novel Framework for Human Action Recognition Using Sequential Learning and Depth Estimated History Images

Human action recognition (HAR) is a challenging task due to the presence of the pose and temporal variations in the action videos. To address these challenges, HAR-Depth is proposed in this paper with sequential and shape learning along with the novel concept of depth history image (DHI). A deep bidirectional long short term memory (DBiLSTM) is constructed for sequential learning to model the temporal relationship existing between the action frames. Action information in each frame is extracted using pre-trained convolutional neural network (CNN). The depth information of each action frame is estimated and projected onto the X-Y plane to form the DHI. During shape learning, the shape information through DHI is used to train a deep pre-trained CNN network. By leveraging the trained knowledge of the pre-trained network, overfitting issue is handled. The finetuned network is used to recognize actions from query DHI images. Data augmentation is adopted to avoid overfitting of the network by virtually increasing the training set. The proposed work is evaluated on publicly available datasets like KTH, UCF sports, JHMDB, UCF101, and HMDB51 and achieves the performance accuracy of 97.67%, 95.00%, 73.13%, 92.97%, and 69.74% respectively. The results on these datasets suggest that the proposed work of this paper performs better in terms of overall accuracy, kappa parameter and precision compared to the other state-of-the-art algorithms present in the earlier reported literature.
Automated diagnosis of breast cancer using parameter optimized kernel extreme learning machine

Early detection and diagnosis is one of the primary steps towards an effective treatment of breast cancer. An automated and reliable computer-aided diagnosis (CAD) model is a suitable alternative to facilitate and ease the diagnosis process. The proposed work focuses on developing an efficacious CAD model that can classify the digital mammograms as normal, benign, or malignant. First, a block-based cross diagonal texture matrix (CDTM) technique is applied to the mammogram ROIs, and then Haralick's features are extracted from each of the ROIs. Then, the kernel principal component analysis (KPCA) technique is used to reduce the dimension of the generated feature vector. Finally, a wrapper-based parameter optimized kernel extreme learning machine (KELM) is proposed wherein the principle of the grasshopper optimization is utilized to determine the optimal values of KELM parameters. Additionally, the proposed wrapper technique is also used to select the important features from the reduced feature vector. The proposed scheme is compared with some of the recently reported CAD models on two standard and publicly available datasets, namely, MIAS and DDSM. From experimental results and analysis, it is noticed that the proposed CAD model yields better results in terms of classification accuracy, and area under curve with a significantly reduced number of features, as compared to that of its counterparts. Additionally, the model is also validated for multi-class classification on the aforementioned datasets reporting an accuracy of 97.49% and 92.61% for MIAS and DDSM datasets, respectively. Further, the proposed model demands minimum computational time than that of its counterparts.
Double-Level Locally Weighted Extreme Learning Machine for Soft Sensor Modeling of Complex Nonlinear Industrial Processes

In order to provide the stable and reliable prediction of key quality variables for industrial processes with complicated nonlinear characteristic, this paper proposes a Double-level Locally Weighted Extreme Learning Machine (DLWELM) based soft sensor modeling method. Firstly, a just-in-time learning based extreme learning machine framework (JITELM) is designed for adaptive nonlinear soft sensor modeling. Then, one double-level similarity measure methodology is presented with considering both the variable importance and the sample distance. At the first level, the mutual information between different input variables and output variable is computed for evaluating the importance of the input variables, which results in the variable weight coefficients. Furthermore, the variable-weighted sample distances are obtained at the second level, which are utilized to build the similarity measure for the relevant samples searching. Lastly, with the relevant samples as the modeling dataset, the locally weighted ELM (LWELM) model is developed, which assigns different weights to the training samples according to their double-level similarity values. Three cases including one numerical system, the industrial debutanizer column plant and the industrial polypropylene production process, are used to test the methods and the results demonstrate that the proposed DLWELM method has higher prediction precision compared to the basic ELM methods.
Deep Graph Learning

Many real data come in the form of non-grid objects, i.e. graphs, from social networks to molecules. Adaptation of deep learning from grid-alike data (e.g. images) to graphs has recently received unprecedented attention from both machine learning and data mining communities, leading to a new cross-domain field---Deep Graph Learning (DGL). Instead of painstaking feature engineering, DGL aims to learn informative representations of graphs in an end-to-end manner. It has exhibited remarkable success in various tasks, such as node/graph classification, link prediction, etc.
A Survey on Deep Learning: Convolution Neural Network (CNN)

Deep learning is a subfield of machine learning and plays a vital role in the area of image processing, natural language processing, computer vision, etc. As compared to traditional machine learning methods, it has a strong ability of self-learning and self-debugging. Convolution neural network (CNN) is the most widely used technique of deep learning for better feature extraction from large datasets. Many researchers adopted CNN for object classification, face recognition, automatic handwritten, etc. In this paper, the detailed concepts behind CNN are discussed with their broad applications.
Cryptocurrency liquidity during extreme price movements: is there a problem with virtual money?

The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrencies—bitcoin, ethereum, ripple, litecoin and dash—and two cryptocurrency indices—Crypto Index (CRIX) and CCI30 Crypto Currencies Index—to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTs' profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.