Reviewerly/exHarmony huggingface dataset:
Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.
Deep Non-Rigid Structure From Motion

Current non-rigid structure from motion (NRSfM) algorithms are mainly limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle. This has hampered the practical utility of NRSfM for many applications within vision. In this paper we propose a novel deep neural network to recover camera poses and 3D points solely from an ensemble of 2D image coordinates. The proposed neural network is mathematically interpretable as a multi-layer block sparse dictionary learning problem, and can handle problems of unprecedented scale and shape complexity. Extensive experiments demonstrate the impressive performance of our approach where we exhibit superior precision and robustness against all available state-of-the-art works in the order of magnitude. We further propose a quality measure (based on the network weights) which circumvents the need for 3D ground-truth to ascertain the confidence we have in the reconstruction.
Deep Single Image Camera Calibration With Radial Distortion

Single image calibration is the problem of predicting the camera parameters from one image. This problem is of importance when dealing with images collected in uncontrolled conditions by non-calibrated cameras, such as crowd-sourced applications. In this work we propose a method to predict extrinsic (tilt and roll) and intrinsic (focal length and radial distortion) parameters from a single image. We propose a parameterization for radial distortion that is better suited for learning than directly predicting the distortion parameters. Moreover, predicting additional heterogeneous variables exacerbates the problem of loss balancing. We propose a new loss function based on point projections to avoid having to balance heterogeneous loss terms. Our method is, to our knowledge, the first to jointly estimate the tilt, roll, focal length, and radial distortion parameters from a single image. We thoroughly analyze the performance of the proposed method and the impact of the improvements and compare with previous approaches for single image radial distortion correction.
A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model

Quantitative risk assessment can play a crucial role in effective decision making about cybersecurity strategies. The Factor Analysis of Information Risk (FAIR) is one of the most popular models for quantitative cybersecurity risk assessment. It provides a taxonomic framework to classify cybersecurity risk into a set of quantifiable risk factors and combines this with quantitative algorithms, in the form of a kind of Monte Carlo (MC) simulation combined with statistical approximation techniques, to estimate cybersecurity risk. However, the FAIR algorithms restrict both the type of statistical distributions that can be used and the expandability of the model structure. Moreover, the applied approximation techniques (including using cached data and interpolation methods) introduce inaccuracy into the FAIR model. To address restrictions of the FAIR model, we develop a more flexible alternative approach, which we call FAIR-BN, to implement the FAIR model using Bayesian Networks (BNs). To evaluate the performance of FAIR and FAIR-BN, we use a MC method (FAIR-MC) to implement calculations of the FAIR model without using any of the approximation techniques adopted by FAIR, thus avoiding the corresponding inaccuracy that can be introduced. We compare the empirical results generated by FAIR and FAIR-BN against a large number of samples generated using FAIR-MC. Both FAIR and FAIR-BN provide consistent results compared with FAIR-MC for general cases. However, the FAIR-BN achieves higher accuracy in several cases that cannot be accurately modelled by the FAIR model. Moreover, we demonstrate that FAIR-BN is more flexible and extensible by showing how it can incorporate process-oriented and game-theoretic methods. We call the resulting combined approach “Extended FAIR-BN” (EFBN) and show that it has the potential to provide an integrated solution for cybersecurity risk assessment and related decision making.
A modular cluster based collaborative recommender system for cardiac patients

In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.
Power Adjusting and Bribery Racing

Mining attacks allow attackers to gain an unfair share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among the most well-known are block withholding (BWH), fork after withholding (FAW), and selfish mining. In this paper, we propose two new strategies: power adjusting and bribery racing, and introduce two novel mining attacks, Power Adjusting Withholding (PAW) and Bribery Selfish Mining (BSM) adopting the new strategies. Both attacks can increase the reward of attackers. Furthermore, we show PAW can avoid the "miner's dilemma" in BWH attacks. BSM introduces a new "venal miner's dilemma", which results in all targets (bribes) willing to help the attacker but getting less reward finally. Quantitative analyses and simulations are conducted to verify the effectiveness of our attacks. We propose some countermeasures to mitigate the new attacks, but a practical and efficient solution remains to be an open problem.
A Graph-Based Framework to Bridge Movies and Synopses

Inspired by the remarkable advances in video analytics, research teams are stepping towards a greater ambition - movie understanding. However, compared to those activity videos in conventional datasets, movies are significantly different. Generally, movies are much longer and consist of much richer temporal structures. More importantly, the interactions among characters play a central role in expressing the underlying story. To facilitate the efforts along this direction, we construct a dataset called Movie Synopses Associations (MSA) over 327 movies, which provides a synopsis for each movie, together with annotated associations between synopsis paragraphs and movie segments. On top of this dataset, we develop a framework to perform matching between movie segments and synopsis paragraphs. This framework integrates different aspects of a movie, including event dynamics and character interactions, and allows them to be matched with parsed paragraphs, based on a graph-based formulation. Our study shows that the proposed framework remarkably improves the matching accuracy over conventional feature-based methods. It also reveals the importance of narrative structures and character interactions in movie understanding. Dataset and code are available at: https://ycxioooong.github.io/projects/moviesyn.
Enhancing Students’ Biology Learning by Using Augmented Reality as a Learning Supplement

The purpose of this study was to investigate the effects of augmented reality (AR) technology on students’ learning outcomes (measured according to Bloom’s cognitive levels) and attitude toward biology. The print book was redesigned by integrating a form of AR into it. A quasi-experimental pretest and posttest designs were used to test the effectiveness of the developed book on learning outcomes and attitude toward biology. In addition, the students’ opinions about the AR technology and the redesigned book were collected. In all, 68 ninth-grade students participated in the study. They were divided into the experimental group, who used the print book and the AR technology as a learning supplement, and the control group, who used the print book only. The results indicated that using AR technology may have the potential to enhance students’ learning outcomes at the analyzing level and their learning attitudes toward biology. The students mentioned that AR could be effective in terms of enhancing their biology learning.
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning

Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. In common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t. sentence quality.
Deep Meta Metric Learning

In this paper, we present a deep meta metric learning (DMML) approach for visual recognition. Unlike most existing deep metric learning methods formulating the learning process by an overall objective, our DMML formulates the metric learning in a meta way, and proves that softmax and triplet loss are consistent in the meta space. Specifically, we sample some subsets from the original training set and learn metrics across different subsets. In each sampled sub-task, we split the training data into a support set as well as a query set, and learn the set-based distance, instead of sample-based one, to verify the query cell from multiple support cells. In addition, we introduce hard sample mining for set-based distance to encourage the intra-class compactness. Experimental results on three visual recognition applications including person re-identification, vehicle re-identification and face verification show that the proposed DMML method outperforms most existing approaches.
Leveraging Linear Decryption: Rate-1 Fully-Homomorphic Encryption and Time-Lock Puzzles

We show how to combine a fully-homomorphic encryption scheme with linear decryption and a linearly-homomorphic encryption schemes to obtain constructions with new properties. Specifically, we present the following new results.
Developing a framework for considering blockchain pilots in the supply chain – lessons from early industry adopters

Purpose There is a lot of interest in blockchain technology in the supply chain, but to date, there is little empirical research to support managerial decision-making. Verhoeven et al. (2018) suggested five screens for ensuring mindful development of use cases for blockchain in the supply chain. This paper aims to extend the Verhoeven et al. ’s (2018) framework and empirical research beyond the use case stage into the pilot stage. Design/methodology/approach Three case studies in different industries (logistics services, consumer products and retail), supply chain positions (manufacturer, carrier, and retailer) and geographies (Europe and USA) are conducted to explore lessons learned by early adopters of blockchain technology in the supply chain. Findings The case studies indicate that the Verhoeven et al. ’s (2018) screens, with one added screen, are helpful in considering “what to adopt.” Based on the insights from the case studies, it is also possible to develop pilot design considerations that can inform “where to start.” Lessons learned include that there is value in scoping pilots in a targeted manner, including the use of existing technology in the pilot (as opposed to replacing existing technology) and that there is the ability to start a pilot fast, provided the existence of executive and stakeholder engagement. Practical implications In addition to the need to be being mindful in considering what use case for blockchain in the supply chain to potentially adopt, mindful consideration of blockchain technology in the supply chain extends into the design of pilots. Six specific design considerations are offered. Originality/value Based on the insights from early adopters in industry, the author extends guidance for the mindful adoption of blockchain in the supply chain beyond the development of use cases, into the design of actual pilots. These insights directly address calls for research from literature (including from Dobrovnik et al. , 2018 and Ferdows, 2018).
Mixed-Distribution-Based Robust Stochastic Configuration Networks for Prediction Interval Construction

It is challenging to develop point prediction models with high accuracy due to that outliers and noise are commonly present in the real-world data. In this context, this article proposes a novel robust stochastic configuration network (SCN) and uses the bootstrap ensemble strategy to construct prediction intervals (PIs). Since the output weights of the original SCN are computed by the least-squares method, which is sensitive to noise with an unknown distribution or outliers, a robust SCN based on a mixture of the Gaussian and Laplace distributions (MoGL-SCN) in the Bayesian framework is proposed. The mixed distributions can effectively characterize the complex distributions of the real-world data, and their heavy-tailed properties can improve the robustness of SCNs. Furthermore, there are no analytical solutions available to obtain the network parameters due to the assumption on the mixed distributions, hence, the parameters of the MoGL-SCN are estimated by the expectation-maximization algorithm. In addition, considering the uncertainties caused by both the model mismatch and noise in the real-world data, a bootstrap ensemble strategy using MoGL-SCN is designed to construct the PIs. The experimental results on two benchmark datasets and a real-world dataset demonstrate the effectiveness of the proposed method in terms of the quality of PIs, prediction accuracy, and robustness.
Deep Graphical Feature Learning for the Feature Matching Problem

The feature matching problem is a fundamental problem in various areas of computer vision including image registration, tracking and motion analysis. Rich local representation is a key part of efficient feature matching methods. However, when the local features are limited to the coordinate of key points, it becomes challenging to extract rich local representations. Traditional approaches use pairwise or higher order handcrafted geometric features to get robust matching; this requires solving NP-hard assignment problems. In this paper, we address this problem by proposing a graph neural network model to transform coordinates of feature points into local features. With our local features, the traditional NP-hard assignment problems are replaced with a simple assignment problem which can be solved efficiently. Promising results on both synthetic and real datasets demonstrate the effectiveness of the proposed method.
Using Mobile Devices for Improving Learning Outcomes and Teachers’ Professionalization

Teaching in higher education is changing due to the influence of technology. More and more technological tools are replacing old teaching methods and strategies. Thus, mobile devices are being positioned as a key tool for new ways of understanding educational practices. The present paper responds to a systematic review about the benefits that mobile devices have for university students’ learning. Using inclusion and exclusion criteria in the Web of Science and Scopus databases, 16 articles were selected to argue why Mobile learning (Mlearning) has become a modern innovative approach. The results point to an improvement in students’ learning through Mlearning, factors that encourage the use of mobile devices in universities have been identified, and effective mobile applications in improving teaching and learning processes have been presented. The inclusion of this methodology requires a new role for teachers, whose characterization is also specified.
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo

One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3D points cloud. This approach relies on photo-consistency to evaluate the goodness of a depth estimate. It generally produces very accurate results; however, the reconstructed model often lacks completeness, especially in correspondence of broad untextured areas where the photo-consistency metrics are unreliable. Assuming the untextured areas piecewise planar, in this paper we generate novel PatchMatch hypotheses so to expand reliable depth estimates in neighboring untextured regions. At the same time, we modify the photo-consistency measure such to favor standard or novel PatchMatch depth hypotheses depending on the textureness of the considered area. We also propose a depth refinement step to filter wrong estimates and to fill the gaps on both the depth maps and normal maps while preserving the discontinuities. The effectiveness of our new methods has been tested against several state of the art algorithms in the publicly available ETH3D dataset containing a wide variety of high and low-resolution images.
Regularized nonnegative matrix factorization with adaptive local structure learning

Due to the effectiveness of Nonnegative Matrix Factorization (NMF) and its graph regularized extensions, these methods have been received much attention from various researchers. Generally, these methods are performed in two separate steps including Laplacian graph construction and the subsequent matrix decomposition.However, the similarity measurement for Laplacian graph is challenging since it's often affected by several factors such as the neighborhood size, choice of similarity metric, etc. As a result, the learned graph may be not suitable, let alone the subsequent matrix decomposition. In this paper, we propose adaptive graph regularized NMF. Different from existing methods, the similarity matrix is automatically learned from the data. The proposed model can simultaneously performs matrix decomposition and similarity learning. By balancing the interactions between both of the two subtasks in our model, each subtask is improved iteratively based on the result of another. Experimental results on benchmark data sets illustrate the effectiveness of our model.
Tractor-Trailer Vehicle Trajectory Planning in Narrow Environments With a Progressively Constrained Optimal Control Approach

Trajectory planning for a tractor-trailer vehicle is challenging due to the non-convex collision avoidance constraints and the unstable kinematics. This article aims to plan precise and optimal trajectory for a car-like tractor that tows standard or general N-trailers in an environment with static obstacles. This trajectory planning scheme can be formulated as a unified optimal control problem, but numerically solving that problem is difficult. Two efforts are made to address the difficulty. First, an extended hybrid A* search algorithm is proposed to provide an initial guess for the numerical optimization. Second, the numerical optimization process is eased through adopting a progressively constrained optimization strategy, which is about sequentially handling the kinematics, collision avoidance constraints, and boundary conditions. Particularly in dealing with the collision avoidance constraints, an adaptively homotopic warm-starting algorithm is proposed, which defines a sequence of subproblems with the obstacle areas adaptively increase towards their nominal scales. Through solving these subproblems in a sequence, the whole collision avoidance difficulties are dispersed and gradually tackled. Comparative simulations are conducted to validate the efficacy of the proposed trajectory planner.
Detecting Offensive Language on Arabic Social Media Using Deep Learning

Offensive content on social media such as verbal attacks, demeaning comments or hate speech has many negative effects on its users. The automatic detection of offensive language on Arabic social media is an important step towards the regulation of such content for Arabic speaking users of social media. This paper presents the results of evaluating the performance of four different neural network architectures for this task: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Bi-LSTM with attention mechanism, and a combined CNN-LSTM architecture. These networks are trained and tested on a labeled dataset of Arabic YouTube comments. We run this dataset through a series of pre-processing steps and use Arabic word embeddings to represent the comments. We also apply Bayesian optimization techniques to tune the hyperparameters of the neural network models. We train and test each network using 5-fold cross validation. The CNN-LSTM achieves the highest recall (83.46%), followed by the CNN (82.24%), the Bi-LSTM with attention (81.51%) and the Bi-LSTM (80.97%).