Reviewerly/exHarmony huggingface dataset:
Shiva's Rangoli

This paper describes the underlying motivation, creation process, and evaluation outcomes of Shiva's Rangoli, a tangible storytelling installation that allows readers to impact the emotional tone of a narrative by sculpting the ambience of their space. Readers interact with a tangible interface that acts as a boundary object between the reader and the fictional world. We discuss how these kinds of interfaces can engage readers to feel like they are a part of the story, endow them with responsibility, and blur the line between real and fictional worlds.
The web is missing an essential part of infrastructure

A proposal for building an index of the Web that separates the infrastructure part of the search engine---the index---from the services part that will form the basis for myriad search engines and other services utilizing Web data on top of a public infrastructure open to everyone.
Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

Summary Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi‐orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy.
Binary classification of imbalanced datasets: The case of CoIL challenge 2000

This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity, sensitivity, precision-recall, and ROC criteria are employed to compare the performance of the methods. Among the methods proposed in this paper, the best one performs much better than the winner of the competition.
A dense multi-path decoder for tissue segmentation in histopathology images

Segmenting different tissue components in histopathological images is of great importance for analyzing tissues and tumor environments. In recent years, an encoder-decoder family of convolutional neural networks has increasingly adopted to develop automated segmentation tools. While an encoder has been the main focus of most investigations, the role of a decoder so far has not been well studied and understood. Herein, we proposed an improved design of a decoder for the segmentation of epithelium and stroma components in histopathology images.The proposed decoder is built upon a multi-path layout and dense shortcut connections between layers to maximize the learning and inference capability. Equipped with the proposed decoder, neural networks are built using three types of encoders (VGG, ResNet and preactived ResNet). To assess the proposed method, breast and prostate tissue datasets are utilized, including 108 and 52 hematoxylin and eosin (H&E) breast tissues images and 224 H&E prostate tissue images.Combining the pre-activated ResNet encoder and the proposed decoder, we achieved a pixel wise accuracy (ACC) of 0.9122, a rand index (RAND) score of 0.8398, an area under receiver operating characteristic curve (AUC) of 0.9716, Dice coefficient for stroma (DICE_STR) of 0.9092 and Dice coefficient for epithelium (DICE_EPI) of 0.9150 on the breast tissue dataset. The same network obtained 0.9074 ACC, 0.8320 Rand index, 0.9719 AUC, 0.9021 DICE_EPI and 0.9121 DICE_STR on the prostate dataset.In general, the experimental results confirmed that the proposed network is superior to the networks combined with the conventional decoder. Therefore, the proposed decoder could aid in improving tissue analysis in histopathology images.
Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers

With the development of deep convolutional neural networks in recent years, the network structure has become more and more complicated and varied, and there are very good results in pattern recognition, image classification, scene classification, and target tracking. This end-to-end learning model relies on the initial large dataset. However, many data are gradually obtained in practical situations, which contradict the deep learning of one-time batch learning. There is an urgent need for an incremental learning approach that can continuously learn new knowledge from new data while retaining what has already been learned. This paper proposes an incremental learning algorithm based on convolutional neural network and support vector data description. CNN and AM-Softmax loss function are used to represent and continuously learn image features. Support vector data description is used to construct multiple hyperspheres for new and old classes of images. Class-incremental learning is achieved by the increment of hyperspheres. The experimental results show that the incremental learning method proposed in this paper can effectively extract the latent features of the image and adapt it to the learning situation of the class-increment. The recognition accuracy is close to batch learning.
Security Applications and Challenges in Blockchain

Blockchain technology is a highly popular yet highly misunderstood concept that is used today and in future applications. To enhance security and privacy, many applications adopt Blockchain. However, there are intrinsic drawbacks and emerging challenges. In this paper, we study popular security applications in Blockchain, present their major problems, as well as other challenges in Blockchain which allows future research to be conducted more efficiently.
Illumination-Invariant Face Recognition With Deep Relit Face Images

Uncontrolled illumination is one of the most significant challenges in face recognition. The performance of state-of-the-art face recognition algorithms drops drastically when measured on datasets with large illumination variations. In this paper, we propose a deep face relighting algorithm and employ it as a data augmentation method to enrich training data with illumination variations. For an input image, the proposed face relighting as data augmentation (FRADA) approach first estimates its 3D morphable model coefficients and spherical harmonic lighting coefficients. Then, it extracts the face normals, face mask, face shading, and face albedo, and renders new face images under random lighting conditions following physically-based image formation theory. Qualitative results demonstrate that FRADA produces more realistic images than the state-of-the-art face relighting algorithm. Quantitative experiments confirm the effectiveness of our relighting approach for face recognition. We successfully enhance the robustness of face templates to illumination variations simply by training face recognition algorithms with our relit images.
Another Robust NMF: Rethinking the Hyperbolic Tangent Function and Locality Constraint

Non-negative matrix factorization (NMF) is a classical data analysis tool for clustering tasks. It usually considers the squared loss to measure the reconstruction error, thus it is sensitive to the presence of outliers. Looking into the literature, most of the existing robust NMF models focus on statistics-based robust estimators with known distribution assumptions. Besides those estimators, whether can we seek another function without the distribution assumption to boost the robustness of NMF? To solve the problem, we propose a robust NMF termed as tanhNMF for short, which rethinks the hyperbolic tangent (tanh) function as a robust loss to evaluate the reconstruction error. Moreover, to capture geometric structure within the data, we devise a locality constraint to regularize tanhNMF to model data locality. Owing to the non-convex tanh function, it is non-trivial to optimize tanhNMF. Following the paradigm of the half-quadratic algorithm, we easily solve an adaptive weighted NMF instead of original tanhNMF. The experiments of face clustering on four popular facial datasets with/without corruptions show that the proposed method achieves the satisfactory performance against several representative baselines including NMF and its robust counterparts. This also implies that the proposed tanh function could serve as an alternative robust loss for NMF.
Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery

Segmentation of epicardial and endocardial boundaries is a critical step in diagnosing cardiovascular function in heart patients. The manual tracing of organ contours in computed tomography angiography (CTA) slices is subjective, time-consuming and impractical in clinical setting. We propose a novel multidimensional automatic edge detection algorithm based on shape priors and principal component analysis (PCA). We have developed a highly customised parametric model for implicit representations of segmenting curves (3D) for left ventricle (LV), right ventricle (RV) and epicardium (Epi) used simultaneously to achieve myocardial segmentation. We have combined these representations in a region-based image modelling framework with high-level constraints enabling the modelling of complex cardiac anatomical structures to automatically guide the segmentation of endo/epicardial boundaries. Test results on 30 short-axis CTA datasets show robust segmentation with error (mean±std mm) of (1.46±0.41), (2.06±0.65) and (2.88±0.59) for LV, RV and Epi, respectively.
Application of DBN for estimating daily solar radiation on horizontal surfaces in Lhasa, China

The need of daily solar radiation (DSR) has been well recognized over the years because many countries consider solar energy into power grids. The essence of this paper is to apply the deep belief network (DBN) to estimating daily solar radiation on horizontal surfaces for Lhasa in China. Measured weather data (wind speed, sunshine duration, air dry-bulb temperature, air relative humidity) which cover the period of 1994–2015 are obtained and devoted to modeling. The statistical indicators (mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R)) show that, the DBN model, optimized by testing the effect of multiple parameters, has more promising result when compared to back propagation (BP) network.
Atomic motion and dipole–dipole effects on the stability of atom–atom entanglement in Markovian/non-Markovian reservoir

In this paper, we consider the entanglement dynamics of two identical qubits (two-level atoms) accompanied by dipole–dipole interaction within a common reservoir in the strong and weak coupling regimes. We suppose that the qubits move in the reservoir which is at zero temperature. Using the time-dependent Schrödinger equation, the state vector of the qubits-reservoir system is obtained by which we can evaluate the concurrence as a suitable measure of entanglement between the two qubits. The results show that by choosing special initial conditions for the qubits, a different dynamical behavior of entanglement is visible in such a way that entanglement protection occurs. Also, we find that the qubit motion in the absence of dipole–dipole interaction leads to preservation or at least more slowly decay of entanglement. However, in the presence of dipole–dipole interaction with the movement of qubits, different results can be observed which depend on the initial states of the qubits, i.e. entanglement may or may not be protected.
A multiple feature construction method based on gravitational search algorithm

One of the most important issues to tackle in data classification has been the existence of non-informative features in feature sets. Therefore, feature construction (FC) is an important pre-processing task to construct discriminating features from the original ones. Gravitational search algorithm (GSA) is a powerful swarm-based metaheuristic algorithm, which has been improved and adapted to represent multiple new constructed features. Most of the swarm-based algorithms entail a population of individuals while one individual would be returned as an optimal solution at the end of the process. In this paper, the solutions’ structure of GSA have been changed in a way that each individual can be considered as a part of the solution, and the final result consists of the whole population. Consequently, each individual is a constructed feature aiming to achieve a population of good features. In other words, the proposed method is a novel multiple feature construction (MFC) method based on the GSA which is called in brief GSAMFC. The experimental results on thirteen standard data sets demonstrate that the proposed GSAMFC is highly beneficial for providing suitable and small feature subsets as well as improving the classifier accuracy. The obtained results of GSAMFC and those of the competing algorithms prove the proficiency of the proposed method.
Reliability-based robust multi-atlas label fusion for brain MRI segmentation

Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic resonance (MR) images. Although a number of label fusion methods have been developed in literature, most of those existing methods fail to address two important problems, i.e., (1) compared with boundary voxels, inner voxels usually have higher probability (or reliability) to be correctly segmented, and (2) voxels with high segmentation reliability (after initial segmentation) can help refine the segmentation of voxels with low segmentation reliability in the target image. To this end, we propose a general reliability-based robust label fusion framework for multi-atlas based MR image segmentation. Specifically, in the first step, we perform initial segmentation for MR images using a conventional multi-atlas label fusion method. In the second step, for each voxel in the target image, we define two kinds of reliability, including the label reliability and spatial reliability that are estimated based on the soft label and spatial information from the initial segmentation, respectively. Finally, we employ voxels with high label-spatial reliability to help refine the label fusion process of those with low reliability in the target image. We incorporate our proposed framework into four well-known label fusion methods, including locally-weighted voting (LWV), non-local mean patch-based method (PBM), joint label fusion (JLF) and sparse patch-based method (SPBM), and obtain four novel label-spatial reliability-based label fusion approaches (called ls-LWV, ls-PBM, ls-JLF, and ls-SPBM). We validate the proposed methods in segmenting ROIs of brain MR images from the NIREP, LONI-LPBA40 and ADNI datasets. The experimental results demonstrate that our label-spatial reliability-based label fusion methods outperform the state-of-the-art methods in multi-atlas image segmentation.
Lithobox

The integration of new digital and physical fabrication tools with fine arts has the potential to provide new outlets for artistic expression, while at the same time raising questions about the role of material and process in artistic practice. In this work, we present Lithobox, a system that translates the traditional ceramic and lighting technique of lithophanes into a means of creating illuminated 3D models through a creative approach that utilizes both digital and tangible construction. Through work sessions with nine artists, we explored how the Lithobox fabrication process impacted the way artists manifest design ideas and engage in creative exploration in crafting. At the TEI arts track, we plan to show our system and the physical lithophanes from our work with artists. The attendees will likely discuss the design, material, and artistic aspects of our exhibit. From these discussions, our goal is to gain insight into beneficial directions for integrating digital technology into traditional fine arts practices.
Edge-Guided Cross-Domain Learning with Shape Regression for Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is a challenging task due to the cross-domain gap between sketch queries and natural images. In this paper, we propose a novel edge-guided cross-domain learning network to reduce the domain gap. In particular, edge maps extracted from natural images are introduced as the bridge between two domains, and the inputs from different domains are embedded into a common feature space. An edge guidance module is proposed to fuse natural images and the corresponding edge maps, which guides the network to generate more discriminative features in the domain alignment process. Meanwhile, a shape regression module is proposed to capture the inherent shape similarity between sketches and natural images. By training the proposed network in an end-to-end process, the sketch and natural image domains can be effectively associated, which potentially overcomes the challenge of the common feature learning for two heterogeneous domains. The experimental results on the SBIR dataset demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.
Nanotechnology and cryptographic protocols: issues and possible solutions

Nanotechnology has provided an immense increment in computational power. This high computational strength is very dangerous for existing cryptographic protocols, and the Data Encryption Standard algorithm is a classic example of that. Thus, it is an urgent requirement that new protocols or mathematical methods be developed for data security that can remain secure from fast modern computers powered by nanotechnology. The authors consider the example of military communication, as data communication security in military communication is a matter of national security. The authors present a new method for message exchange for military communication that has never been presented until now. This novelty creates a hard task for intruders, and the authors use steganography as a supporting tool for cryptography. The security of encrypted data increases when the data are hidden in a carrier file. Nobody knows (except the sender and the intended receiver) that there are some hidden data. The authors compare different algorithms for size restrictions because size is an important factor for steganograms and that is the only chance of guessing that the open file contains something suspicious (hidden encrypted data).
Machine learning methods in quantum computing theory

Classical machine learning theory and theory of quantum computations are among of the most rapidly developing scientific areas in our days. In recent years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. The quantum machine learning includes hybrid methods that involve both classical and quantum algorithms. Quantum approaches can be used to analyze quantum states instead of classical data. On other side, quantum algorithms can exponentially improve classical data science algorithm. Here, we show basic ideas of quantum machine learning. We present several new methods that combine classical machine learning algorithms and quantum computing methods. We demonstrate multiclass tree tensor network algorithm, and its approbation on IBM quantum processor. Also, we introduce neural networks approach to quantum tomography problem. Our tomography method allows us to predict quantum state excluding noise influence. Such classical-quantum approach can be applied in various experiments to reveal latent dependence between input data and output measurement results.
A Temporal Recommendation Mechanism Based on Signed Network of User Interest Changes

In recommender systems, one of critical tasks is to help users find their interested items among huge amount of items. The development of recommendation methods and techniques has driven many real-world recommendation applications. Desirable recommendations are vital both to the target users and the recommended items. Mining meaningful interest information from different time spans is crucial for recommendation precision. In this paper, the relations between different time spans are adopted to construct the signed networks and imitate users interest changes. First, we initially utilize temporal score information to divide different time spans. Then, the signed networks are formed at different time steps referring to users interest changes. Furthermore, considering the characteristics of signed networks, we define a new similarity measurement to grasp the common interest between two adjacent signed networks. A new adjacent matrix is obtained by weighting the network adjacent matrices of different time steps. According to the constructed dynamic evolutionary clustering model in a signed network, the nodes are divided into different clusters. Finally, the predicted ratings are calculated in each subclass instead of the entire system, which reduces the computational cost greatly. The extensive simulations are conducted based on two real-world datasets, Movielens and CiaoDVD, for demonstrating that the recommend accuracy is significantly improved using our scheme.
Detecting clear sky images

Many solar forecast algorithms based on ground based sky imagery apply the red-blue ratio (RBR) method to classify image pixels as clear or cloudy, by comparing the current image with the corresponding image from a clear sky library (CSL). The CSL needs to be updated regularly due to changes in clear sky readings over time caused by aerosols and imager dome properties. This clear sky library is typically created by visually scrutinizing daily sky videos and selecting appropriate clear sky periods. This practice takes a significant amount of time and manual intervention can result in human errors. To avoid this, an automated CSL algorithm (ACSL) was developed which filters each image for clear sky features including maximum green pixel brightness, average RBR, and red channel difference by pixel with respect to the previous image. The root mean square difference (RMSD) between the image RBR of the manually created CSL and the ACSL for November and April 2013 at UC San Diego were observed to be less than 6% over the full range of solar zenith angles. The ACSL was found to be more representative of clear conditions than its manual counterpart.