Reviewerly/exHarmony huggingface dataset:
Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer

One of the potential applications of a quantum computer is solving quantum chemical systems. It is known that one of the fastest ways to obtain somewhat accurate solutions classically is to use approximations of density functional theory. We demonstrate a general method for obtaining the exact functional as a machine learned model from a sufficiently powerful quantum computer. Only existing assumptions for the current feasibility of solutions on the quantum computer are used. Several known algorithms including quantum phase estimation, quantum amplitude estimation, and quantum gradient methods are used to train a machine learned model. One advantage of this combination of algorithms is that the quantum wavefunction does not need to be completely re-prepared at each step, lowering a sizable pre-factor. Using the assumptions for solutions of the ground-state algorithms on a quantum computer, we demonstrate that finding the Kohn-Sham potential is not necessarily more difficult than the ground state density. Once constructed, a classical user can use the resulting machine learned functional to solve for the ground state of a system self-consistently, provided the machine learned approximation is accurate enough for the input system. It is also demonstrated how the classical user can access commonly used time- and temperature-dependent approximations from the ground state model. Minor modifications to the algorithm can learn other types of functional theories including exact time- and temperature-dependence. Several other algorithms--including quantum machine learning--are demonstrated to be impractical in the general case for this problem.
Binary chemical reaction optimization based feature selection techniques for machine learning classification problems

Feature selection is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy. Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naïve Bayes, decision tree, and random forest are considered for classification tasks. Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier.
Big Data Privacy and Challenges for Machine Learning

The field of Big Data is expanding at an alarming rate since its inception in 2012. The excessive use of Social Networking Sites, collection of Data from Sensors for analysis and prediction of future events, improvement in Customer Satisfaction on Online S hopping portals by monitoring their past behavior and providing them information, items and offers of their interest instantaneously, etc had led to this rise in the field of Big Data. This huge amount of data, if analyzed and processed properly, can lead to decisions and outcomes that would be of great values and benefits to organizations and individuals. Security of Data and Privacy of User is of keen interest and high importance for individuals, industry and academia. Everyone ensure that their Sensitive information must be kept away from unauthorized access and their assets must be kept safe from security breaches. Privacy and Security are also equally important for Big Data and here, it is typical and complex to ensure the Privacy and Security, as the amount of data is enormous. One possible option to effectively and efficiently handle, process and analyze the Big Data is to make use of Machine Learning techniques. Machine Learning techniques are straightforward; applying them on Big Data requires resolution of various issues and is a challenging task, as the size of Data is too big. This paper provides a brief introduction to Big Data, the importance of Security and Privacy in Big Data and the various challenges that are required to overcome for applying the Machine Learning techniques on Big Data.
Kernel-phase analysis: Aperture modeling prescriptions that minimize calibration errors

Kernel-phase is a data analysis method based on a generalization of the notion of closure-phase invented in the context of interferometry, but that applies to well corrected diffraction dominated images produced by an arbitrary aperture. The linear model upon which it relies theoretically leads to the formation of observable quantities robust against residual aberrations. In practice, detection limits reported thus far seem to be dominated by systematic errors induced by calibration biases not sufficiently filtered out by the kernel projection operator. This paper focuses on the impact the initial modeling of the aperture has on these errors and introduces a strategy to mitigate them, using a more accurate aperture transmission model. The paper first uses idealized monochromatic simulations of a non trivial aperture to illustrate the impact modeling choices have on calibration errors. It then applies the outlined prescription to two distinct data-sets of images whose analysis has previously been published. The use of a transmission model to describe the aperture results in a significant improvement over the previous type of analysis. The thus reprocessed data-sets generally lead to more accurate results, less affected by systematic errors. As kernel-phase observing programs are becoming more ambitious, accuracy in the aperture description is becoming paramount to avoid situations where contrast detection limits are dominated by systematic errors. Prescriptions outlined in this paper will benefit any attempt at exploiting kernel-phase for high-contrast detection.
API recommendation for the development of Android App features based on the knowledge mined from App stores

To improve the efficiency, developers tend to use APIs to avoid reinventing wheels in the development of Apps. However, there are thousands of APIs for various purposes, so it is difficult for developers to identify suitable APIs according to the functionalities to be realized. App stores manage millions of products, which embody the experience and wisdom of developers, and they provide valuable data resource for solving this problem. By summarizing the API usage for the same or similar functionalities in Apps, reusable knowledge can be mined for the API recommendation. In this paper, we utilize the data resource in App stores and provide an API recommendation method for the development of Android Apps. Firstly, by using UI elements as the bridge, we establish mapping relationships between functionalities and APIs. Secondly, we build a framework to describe functionalities of Apps in the same category, and utilize relationships between functionalities and APIs to construct the API knowledge for each node in the framework. Thirdly, we identify nodes according to queried features and show the API knowledge to developers by giving recommendation lists. We conducted experiments based on Google Play, and the result shows that our method has a good recommendation performance.
Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches.
Nonpairwise Interactions Induced by Virtual Transitions in Four Coupled Artificial Atoms

The field of quantum information has matured and various protocols implementing a quantum computer are being pursued. Most similar to a classical computer is the circuit model. In 2007 Aharonov et al. showed the equivalence between the circuit model and a quantum annealer, and with this proofed the universality of quantum annealing. Here the system starts in an easily preparable ground state and evolves adiabatically to a final ground state which yields the solution of the computational problem. However, equivalence with the circuit model requires multi-local interactions, i.e. interaction terms involving more than two subsystems. Natural interactions are only two-local, hence the construction or simulation of higher order couplers is indispensable for a universal quantum annealer. Also, four-local couplers allow compact implementation of error correction with the Bacon-Shor code. Four-local interactions can further serve as a tool for basic research. Here we show that in a specific flux qubit coupler design without ancilla qubits, four body interactions are induced by virtual coupler excitations. For specific parameter regimes they are even the leading effect and can be tuned up to the GHz range.
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching

A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-DRM .
AMS: generating AutoML search spaces from weak specifications

We consider a usage model for automated machine learning (AutoML) in which users can influence the generated pipeline by providing a weak pipeline specification: an unordered set of API components from which the AutoML system draws the components it places into the generated pipeline. Such specifications allow users to express preferences over the components that appear in the pipeline, for example a desire for interpretable components to appear in the pipeline. We present AMS, an approach to automatically strengthen weak specifications to include unspecified complementary and functionally related API components, populate the space of hyperparameters and their values, and pair this configuration with a search procedure to produce a strong pipeline specification: a full description of the search space for candidate pipelines. ams uses normalized pointwise mutual information on a code corpus to identify complementary components, BM25 as a lexical similarity score over the target API's documentation to identify functionally related components, and frequency distributions in the code corpus to extract key hyperparameters and values. We show that strengthened specifications can produce pipelines that outperform the pipelines generated from the initial weak specification and an expert-annotated variant, while producing pipelines that still reflect the user preferences captured in the original weak specification.
Effective Performance of Bins Approach for Classification of Malaria Parasite using Machine Learning

Malaria is a severe infectious disease transferred through the bite of an infected mosquito caused by a blood parasite of the genus Plasmodium. In the past, conventional microscopy techniques have proven to be time-consuming and had observed a lack of differentiation due to poor accuracy and a few algorithms used. In this paper, our approach primarily focuses on image processing techniques to process and enhance stained thin blood smear images for feature extraction, as well as machine learning techniques for the final classification of feature space. Our emphasis is also to address the drawbacks, as mentioned earlier, by taking input cell images and performing classification using CNN. As an alternate approach, we have also computed color features using a novel Bins Approach Algorithm, statistical features using color moments, and texture features using GLCM, which also equally played a pivotal role in feature extraction for classification. Further, these images are classified into parasitized and uninfected cells by applying machine learning classifiers such as Linear SVM, Random Forest Algorithm (RNN), and KNN over feature space. The proposed algorithms have been experimented using the subset of Lister Hill National Center for Biomedical Communication (LHNCBC) dataset, a division of the National Library of Medicine (NLM). The performance of the algorithms is evaluated and compared using different parameters like accuracy, precision, recall, and F1-score. The proposed application of Bins Approach in malaria parasite detection has proved better in terms of all parameters as compared to the other existing algorithms.
Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.
PoTS: A Secure Proof of TEE-Stake for Permissionless Blockchains

Proof of Stake (PoS) blockchain protocols emerged as a promising alternative to the largely energy-wasteful proof of work mechanisms currently in place. In contrast to computing power, however, “stake” is a virtual resource that can be replicated or reused, opening the door to attack vectors that have no counterpart in a PoW setting, and are much harder to defeat. We present PoTS (Proof of TEE-Stake), a novel PoS protocol that leverages properties of trusted execution environments (TEEs) to limit the attack surface of malicious validators, and employs techniques such as forward security to guarantee protection against posterior-corruption attacks. We show that PoTS is secure against nothing at stake, grinding, and long range attacks down to realistic hardware assumptions on TEE and well-established cryptographic assumptions, and retains reasonable security even in face of compromised TEEs. We evaluate the performance of our proposal by means of implementation. Our evaluation results demonstrate that PoTS offers an excellent trade-off between security and performance.
Large Batch Optimization for Object Detection: Training COCO in 12 minutes

Most of existing object detectors usually adopt a small training batch size (e.g. 16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure. In this paper, we propose a versatile large batch optimization framework for object detection, named LargeDet, which successfully scales the batch size to larger than 1K for the first time. Specifically, we present a novel Periodical Moments Decay LAMB (PMD-LAMB) algorithm to effectively reduce the negative effects of the lagging historical gradients. Additionally, the Synchronized Batch Normalization (SyncBN) is utilized to help fast convergence. With LargeDet, we can not only prominently shorten the training period, but also significantly improve the detection accuracy of sparsely annotated large-scale datasets. For instance, we can finish the training of ResNet50 FPN detector on COCO within 12 min. Moreover, we achieve 12.2% mAP@0.5 absolute improvement for ResNet50 FPN on Open Images by training with batch size 640.
An Approach towards Position-Independent Human Activity Recognition Model based on Wearable Accelerometer Sensor

The continuous progress in wearable sensing technologies has motivated the researchers to develop novel models for human activity and behavior monitoring. As wearable sensors possess more liberty in their placement at multiple positions on the user’s body to track human motion patterns, hence, they have been extensively utilized in activity recognition systems. However, wearable inertial sensors are prone to their position and orientation sensitivity, thus leading to poor recognition performance in real-time scenarios. Therefore, in this study, we address the problem of position-independent human activity recognition using the wearable sensor. In this aspect, we propose a set of linear and non-linear transformations for 3D-sensor data to minimize the position and orientation sensitivity of the inertial sensor. We also present a feature extraction framework to efficiently recognize human activities independent of any sensor position. Finally, we validate our proposed scheme using the PAMAP dataset, which achieves the best average performance of 94.7% and 91.7% for position-dependent and position-independent activity recognition.
Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex scenes with severe occlusion, deformation, and low resolution and cannot achieve long-term tracking. To improve the accuracy of the tracking algorithm in complex scenes, an adaptive kernel correlation filter algorithm is proposed. First, a multifeature complementary scheme is proposed that linearly weights the responses of the histogram of oriented gradient (HOG) features and color features and learns a target position estimation model to realize target position estimation. Then, an adaptive scale model for estimating the scale transformation of the target is learned by extracting the HOG features of the object. Finally, according to occlusion judgment criteria, the Kalman filter is introduced to correct the position of the tracking target. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on TC-128/OTB2015 benchmarks. Extensive experimental results illustrate that the proposed tracker achieves competitive performance compared with state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.899 and 0.635, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. This study provides references for computer vision processing, such as image retrieval, behavior analysis, and intelligent driving.
Maximum a posterior based level set approach for image segmentation with intensity inhomogeneity

Intensity inhomogeneity is an unavoidable obstacle in image segmentation, which causes inaccuracy in object extraction. Generally, the approach to tackling intensity inhomogeneity is constructing a bias field descriptor which may lead to corruption of image intensity. In this paper, we propose a novel level set model based on maximum a posterior principle. To properly collect the objectslocal information, the proposed method utilizes Gaussian distribution to model the conditional probability of image intensity within specific patches. To construct the prior information, we then model the intensity inhomogeneity as Gaussian distribution whose mean is 1 and whose variance is the same as image intensity. Finally, the maximum a posterior based energy functional combined local image information and adequate prior information is defined. In addition, our method can be adopted and transformed into the state-of-the-art methods. To validate its effectiveness and performance, we compare our method with popular deep learning methods and classical level set methods. The roubstness analysis of initial contour, noise and intensity bias is given. Experimental results show our method achieves outstanding adaptability and stability.
LDPNet: A Lightweight Densely Connected Pyramid Network for Real-Time Semantic Segmentation

A deep convolutional neural network has been widely used in image semantic segmentation in recent years, its deployment on mobile terminals, however is limited by its high computational costs.Given the slow inference speed and large memory usage of deep convolutional neural networks, we propose a lightweight and densely connected pyramid network (LDPNet) for real-time semantic segmentation.Firstly, a densely connected atrous pyramid (DCAP) module is constructed in the encoding process to extract multi-scale context information for forwarding propagation, strengthen the reuse of features, and offset the spatial information lost in the down-sampling process of the feature map.Secondly, a cross-fusion (CF) module embedded in each other during the decoding process is proposed, which uses high-level semantic features to effectively guide the fusion of low-level spatial details while strengthening context information.Our network is tested on two complex urban road scene data sets.Among them, experiments on the Cityscapes data set show that our structure has 87 frames per second (FPS) on a single NVIDIA GTX1080Ti GPU.The Mean Intersection over Union (mIoU) reaches 71.1%, and the parameter is only 0.8M.Compared with the existing similar networks, the new system achieves a state-of-the-art trade-off between efficiency and accuracy.