Reviewerly/exHarmony huggingface dataset:
Combining clustering of variables and feature selection using random forests

Standard approaches to tackle high-dimensional supervised classification problem often include variable selection and dimension reduction procedures. The novel methodology proposed in this paper combines clustering of variables and feature selection. More precisely, hierarchical clustering of variables procedure allows to build groups of correlated variables in order to reduce the redundancy of information and summarizes each group by a synthetic numerical variable. Originality is that the groups of variables (and the number of groups) are unknown a priori. Moreover the clustering approach used can deal with both numerical and categorical variables (i.e. mixed dataset). Among all the possible partitions resulting from dendrogram cuts, the most relevant synthetic variables (i.e. groups of variables) are selected with a variable selection procedure using random forests. Numerical performances of the proposed approach are compared with direct applications of random forests and variable selection using random forests on the original p variables. Improvements obtained with the proposed methodology are illustrated on two simulated mixed datasets (cases n>p and n<p, where n is the sample size) and on a real proteomic dataset. Via the selection of groups of variables (based on the synthetic variables), interpretability of the results becomes easier.
Exploring and analyzing the dark Web: A new alchemy

The Tor network is an encrypted network that allows anonymous access to the Internet for its users. The Tor network also hosts hidden services which constitute the infamous dark Web. These hidden services are used to carry out activities that are otherwise illegal and unethical on the surface Web. These activities include distribution of child pornography, access to illegal drugs, and the sale of weapons. While Tor hidden services provide a platform for uncensored ventures and a free expression of thoughts, they are outnumbered by grey activities taking place. In this paper, we have collected the addresses of about 25,742 hidden services and analyze the data for 6,227 available services with the help of custom-made crawler in Python. We analyzed the dataset and manually classify the data into 31 different categories to identify the nature of content available on the dark Web. The results indicate that a large share of hidden services provide illegal content along with a large number of scam sites. Non-English content was also studied and categorized. Russian was the leading language of the dark Web after English and hidden services having forums and blogs were predominantly present over other content.
BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks

Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is suboptimal for the task, dataset, or question being asked? What if instead of parameterizing and steering this model, a different model provides a better fit? This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multimodel steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multimodel steering.
Securing DNSSEC Keys via Threshold ECDSA from Generic MPC

Deployment of DNSSEC, although increasing, still suffers from many practical issues that results in a false sense of security. While many domains outsource zone management, they also have to outsource DNSSEC key management to the DNS operator, making the operator an attractive target for attackers. Moreover, DNSSEC does not provide any sort of protection in the case the operator itself decides to serve false information, for example, if it gets compromised. In this work, we show how to use techniques from threshold ECDSA: (1) to protect keys such that domains do not reveal their signing keys to a DNS operator, and (2) to protect the operational integrity of DNS operator. As a result of being highly specialized, prior work on threshold ECDSA has focused on a limited set of threat models, and none have so far considered techniques to amortize signature generation. Our work takes a different approach and presents a generic technique for obtaining a threshold ECDSA protocol from any secure multiparty computation protocol that works over an appropriate finite field. We show how this technique lends itself to very efficient threshold signing protocols by comparing it against state-of-the-art protocols from both academia and industry. For similar threat models, our protocols are as fast as the previous best protocol in terms of signing, and up to an order of magnitude faster for key generation on a fast network. Finally, we show how to integrate our application into a widely used DNS management software and demonstrate through experiments the overhead compared to traditional DNSSECs.
A Novel Approach to Quality-of-Service Provisioning in Trusted Relay Quantum Key Distribution Networks

In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions.
Noise-resilient variational hybrid quantum-classical optimization

Variational hybrid quantum-classical optimization is one of the most promising avenues to show the advantages of noisy intermediate-scale quantum computers in solving hard problems, such as finding the minimum-energy state of a Hamiltonian or solving some machine-learning tasks. In these devices, noise is unavoidable and impossible to error correct, yet its role in the optimization process is not well understood, especially from the theoretical viewpoint. Here we consider a minimization problem with respect to a variational state, iteratively obtained via a parametric quantum circuit, taking into account both the role of noise and the stochastic nature of quantum measurement outcomes. We show that the accuracy of the result obtained for a fixed number of iterations is bounded by a quantity related to the quantum Fisher information of the variational state. Using this bound, we study the convergence property of the quantum approximate optimization algorithm under realistic noise models, showing the robustness of the algorithm against different noise strengths.
SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolution but lower spectral resolution than the other bands. In the field of remote sensing, there is a long tradition of applying pan-sharpening to satellite images, i.e. bringing the multispectral bands to the higher spatial resolution by merging them with the panchromatic band. To our knowledge there are so far no approaches to super-resolution which take advantage of the panchromatic band. In this paper we propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles in order to create super-resolved analytic images. The derived quality metrics show that the method improves information content of the processed images. We compare the results created by four CNN architectures, with RedNet30 performing best.
Person Search by Separated Modeling and A Mask-Guided Two-Stream CNN Model

In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification (re-ID). Instead of sharing representations in a single joint model, we find that separating detector and re-ID feature extraction yields better performance. In order to extract more representative features for each identity, we segment out the foreground person from the original image patch. We propose a simple yet effective re-ID method, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams. We also propose a Confidence Weighted Stream Attention method which further re-adjusts the relative importance of the two streams by incorporating the detection confidence. Furthermore, we simplify the whole pipeline by incorporating semantic segmentation into the re-ID network, which is trained by bounding boxes as weakly-annotated masks and identification labels simultaneously. From the experiments on two standard person search benchmarks i.e. CUHK-SYSU and PRW, we achieve mAP of 83.3% and 32.8% respectively, surpassing the state of the art by a large margin. The extensive ablation study and model inspection further justifies our motivation.
EXPLORING APPLICATIONS OF BLOCKCHAIN TECHNOLOGY IN THE CONSTRUCTION INDUSTRY

Blockchain provides a secure decentralized information management system that can solve many common problems facing the construction industry. The loose structure of the construction industry, the way that public and private projects are tendered, and the supply chain system it uses for material and service delivery provide unique challenges and problems. New information technology management systems such as BIM and RFID are used to address some of these issues, though not completely. Blockchain technology can be used to further improve the information management systems in construction, provide more automation and mitigate many possible legal conflicts by default. Implementation of blockchain technology in the construction industry can also result in the use of smart contracts with fewer administrative struggles, improve the flow of the project, material, and service delivery, and increase the security and currentness of BIM or project documents. This study aims to explore the applications of blockchain technology in improving the construction industry's information management systems. It is concluded that not only the blockchain technology has potential in addressing some of the common problems in the construction industry but also it is adaptable to the construction industry structure and the way it is practiced. Thus, blockchain technology is a viable option for adaptation in the construction industry.
Blockchain-Based Distributed Patient-Centric Image Management System

In recent years, many researchers have focused on developing a feasible solution for storing and exchanging medical images in the field of health care. Current practices are deployed on cloud-based centralized data centers, which increase maintenance costs, require massive storage space, and raise privacy concerns about sharing information over a network. Therefore, it is important to design a framework to enable sharing and storing of big medical data efficiently within a trustless environment. In the present paper, we propose a novel proof-of-concept design for a distributed patient-centric image management (PCIM) system that is aimed to ensure safety and control of patient private data without using a centralized infrastructure. In this system, we employed an emerging Ethereum blockchain and a distributed file system technology called InterPlanetary File System (IPFS). Then, we implemented an Ethereum smart contract called the patientcentric access control protocol to enable a distributed and trustworthy access control policy. IPFS provides the means for decentralized storage of medical images with global accessibility. We describe how the PCIM system architecture facilitates the distributed and secured patient-centric data access across multiple entities such as hospitals, patients, and image requestors. Finally, we deployed a smart contract prototype on an Ethereum testnet blockchain and evaluated the proposed framework within the Windows environment. The evaluation results demonstrated that the proposed scheme is efficient and feasible.
Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off

When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers. Straggling workers can be tolerated by assigning redundant computations and coding across data and computations, but in most existing schemes, each non-straggling worker transmits one message per iteration to the parameter server (PS) after completing all its computations. Imposing such a limitation results in two main drawbacks; over-computation due to inaccurate prediction of the straggling behaviour, and under-utilization due to treating workers as straggler/non-straggler and discarding partial computations carried out by stragglers. In this paper, to overcome these drawbacks, we consider multi-message communication (MMC) by allowing multiple computations to be conveyed from each worker per iteration, and design straggler avoidance techniques accordingly. Then, we analyze how the proposed designs can be employed efficiently to seek a balance between the computation and communication latency to minimize the overall latency. Furthermore, through extensive simulations, both model-based and real implementation on Amazon EC2 servers, we identify the advantages and disadvantages of these designs in different settings, and demonstrate that MMC can help improve upon existing straggler avoidance schemes.
Interpretable neural networks based on continuous-valued logic and multicriteria decision operators

Combining neural networks with continuous logic and multicriteria decision-making tools can reduce the black-box nature of neural models. In this study, we show that nilpotent logical systems offer an appropriate mathematical framework for hybridization of continuous nilpotent logic and neural models, helping to improve the interpretability and safety of machine learning. In our concept, perceptrons model soft inequalities; namely membership functions and continuous logical operators. We design the network architecture before training, using continuous logical operators and multicriteria decision tools with given weights working in the hidden layers. Designing the structure appropriately leads to a drastic reduction in the number of parameters to be learned. The theoretical basis offers a straightforward choice of activation functions (the cutting function or its differentiable approximation, the squashing function), and also suggests an explanation to the great success of the rectified linear unit (ReLU). In this study, we focus on the architecture of a hybrid model and introduce the building blocks for future applications in deep neural networks.
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising

Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explicitly learn this process with deep neural networks. We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. The proposed model naturally adapts to image structures and can effectively improve the denoised results. Furthermore, we develop a spatio-temporal pixel aggregation network for video denoising to efficiently sample pixels across the spatio-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes. In addition, we introduce a new regularization term for effectively training the proposed video denoising model. We present extensive analysis of the proposed method and demonstrate that our model performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.
On Computational Aspects of Krawtchouk Polynomials for High Orders

Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method for computing discrete Krawtchouk polynomial coefficients swiftly and efficiently. The presented method proposes a new initial value that does not tend to be zero as the polynomial size increases. In addition, a combination of the existing recurrence relations is presented which are in the n- and x-directions. The utilized recurrence relations are developed to reduce the computational cost. The proposed method computes approximately 12.5% of the polynomial coefficients, and then symmetry relations are employed to compute the rest of the polynomial coefficients. The proposed method is evaluated against existing methods in terms of computational cost and maximum size can be generated. In addition, a reconstruction error analysis for image is performed using the proposed method for large signal sizes. The evaluation shows that the proposed method outperforms other existing methods.
Application of Quantum Computing to Biochemical Systems: A Look to the Future

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.
METTLE: A METamorphic Testing Approach to Assessing and Validating Unsupervised Machine Learning Systems

Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users' requirements and specific application scenarios$\,/\,$contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a $\textbf{MET}$amorphic $\textbf{T}$esting approach to assessing and validating unsupervised machine $\textbf{LE}$arning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users' generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering's perspective, rather than a designer's or implementor's perspective, who normally adopts a theoretical approach.
Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.