Reviewerly/exHarmony huggingface dataset:
Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

Convolutional Neural Networks (CNNs) are widely adopted in the automatic analysis of abnormalities in medical imaging applications. Although CNN structures are robust feature extractors, there are two problems. First, simultaneous classification and segmentation of abnormal regions in medical images is a crucial and difficult task. Second, the implementation of CNN’s with high computational complexity in portable devices imposes power and resource constraints. To address these problems, we propose a bifurcated structure with one branch performing classification, and the other performs the segmentation. Initially, separate network structures are trained for each abnormality separately and then primary parts of these networks are merged. The bifurcated structure has a shared part, which works for all abnormalities. One branch of the final structure has sub-networks for segmentation of different abnormalities, and the other branch has separate sub-networks, each is designed for classification of a specific abnormality. Results of the classification and segmentation are fused to obtain the classified segmentation map. The proposed framework is simulated using four frequent gastrointestinal abnormalities as well as three dermoscopic lesions. Properties of the bifurcated network such as low complexity and resource sharing, make it suitable to be implemented as a part of portable medical imaging devices.
Image analysis by log-polar Exponent-Fourier moments

Moments, as a popular class of the global invariant image descriptors, have been widely used in image analysis, pattern recognition and computer vision applications. Exponent-Fourier moments (EFMs) are a new set of orthogonal moments based on exponential functions, which are suitable for image analysis and rotation invariant pattern recognition. However, EFMs lack natively the scaling-invariant property. In addition, they always suffer from high time complexity, numerical instability, and reconstruction error, especially for higher order of moments. In this paper, we introduce a class of scaling and rotation-invariant orthogonal moments, named Log-Polar Exponent-Fourier moments (LPEFMs), by extending the classical EFMs to the log-polar coordinates. Firstly, we redefined the EFMs' basis functions in log-polar domain instead of Cartesian/polar coordinate domain in order to obtain the scaling-invariant property. Then, we develop a new framework for computing the LPEFMs by using pseudo-polar Fourier transform and frequency domain interpolation, which result in better image representation capability, numerical stability, and computational speed. Compared with the classical EFMs, the proposed LPEFMs have four advantages, scaling invariance, speed, accuracy and stability. Theoretical analysis and simulation results are provided to validate the proposed image moment and to compare its performance with previous works.
An Experimental Analysis of Machine Learning Classification Algorithms on Biomedical Data

Enormous growth of data in biomedical engineering domain has posed a big challenge for data analysis and processing. This massive volume of biomedical data leverage leads to a great challenge. To address this issue, this paper compares the performance measures of various classification algorithms of machine learning on biomedical data. The various classification algorithms such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Support Vector Machine (L-SVM), Radial Basis Function Support Vector Machine (RBF-SVM), Polynomial Support Vector Machine (P-SVM), Random Forest (RF), and Adaboost are used for classification process to measure the classification accuracy with 14 number of biomedical datasets. It is observed that the nature of dataset has very strong impact on the performance of the classifiers. The performance of all the aforesaid classification algorithms are analyzed on 14 biomedical datasets and observed that the Adaboost classifier outperforms than rest of the classifiers.
G-mean based extreme learning machine for imbalance learning

Although extreme learning machine (ELM) provides better generalization performance at a much faster learning speed than traditional learning algorithms, the classical ELM can not obtain ideal results for the imbalanced data problem. In this paper, in order to conquer the learning capability of the classical ELM for an imbalance data learning, we define a new cost function of ELM optimization problem based on G-mean widely used as evaluation metric in imbalance data learning. We perform experiments on standard classification datasets which consist of 58 binary datasets and 11 multi-class datasets with the different degrees of the imbalance ratio. Experimental results show that proposed algorithm can improve the classification performance significantly compared with other state-of-the-art methods. We also demonstrate that our proposed algorithm can achieve high accuracy in representation learning by performing experiments on YouTube-8M with feature representation from convolutional neural networks. Statistical results indicate that the proposed approach not only outperforms the classical ELM, but also yields better or at least competitive results compared with several state-of-the-art class imbalance learning approaches.
Coherence in logical quantum channels

We study the effectiveness of quantum error correction against coherent noise. Coherent errors (for example, unitary noise) can interfere constructively, so that in some cases the average infidelity of a quantum circuit subjected to coherent errors may increase quadratically with the circuit size; in contrast, when errors are incoherent (for example, depolarizing noise), the average infidelity increases at worst linearly with circuit size. We consider the performance of quantum stabilizer codes against a noise model in which a unitary rotation is applied to each qubit, where the axes and angles of rotation are nearly the same for all qubits. In particular, we show that for the toric code subject to such independent coherent noise, and for minimal-weight decoding, the logical channel after error correction becomes increasingly incoherent as the length of the code increases, provided the noise strength decays inversely with the code distance. A similar conclusion holds for weakly correlated coherent noise. Our methods can also be used for analyzing the performance of other codes and fault-tolerant protocols against coherent noise. However, our result does not show that the coherence of the logical channel is suppressed in the more physically relevant case where the noise strength is held constant as the code block grows, and we recount the difficulties that prevented us from extending the result to that case. Nevertheless our work supports the idea that fault-tolerant quantum computing schemes will work effectively against coherent noise, providing encouraging news for quantum hardware builders who worry about the damaging effects of control errors and coherent interactions with the environment.
Cloud Marginal Resource Allocation: A Decision Support Model

One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.
Effects of Personality Traits on Pull Request Acceptance

In this paper, we examine the influence of personality traits of developers on the pull request evaluation process in GitHub. We first replicate Tsay <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s work that examined the influence of social factors (e.g., ‘social distance’) and technical factors (e.g., test file inclusion) for evaluating contributions, and then extend it with personality based factors. In particular, we extract the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) of developers from their online digital footprints, such as pull request comments. We analyze the personality traits of 16,935 active developers from 1,860 projects and compare their relative importance to other non-personality factors from past research, in the pull request evaluation process. We find that pull requests from authors (requesters) who are more open and conscientious, but less extroverted, have a higher chance of approval. Furthermore, pull requests that are closed by developers (closers) who are more conscientious, extroverted, and neurotic, have a higher likelihood of acceptance. The larger the difference in personality traits between the requester and the closer, the more positive effect it has on pull request acceptance. Finally, although the effect of personality traits is significant and comparable to technical factors, we find that social factors are still more influential on the likelihood of pull request acceptance.
Data encryption based blockchain and privacy preserving mechanisms towards big data

Blockchain is a key technique which can support Bitcoin. Blockchain is a decentralized infrastructure that uses chained data structure to verify and store data, and uses distributed node consensus mechanism to generate and update data. Blockchain has become a hot research topic since its attributes of decentralization, verifiability and anti-tampering. To stimulate the development of Blockchain, we conduct a comprehensive research on Blockchain. Specifically, we discuss various mainstream consensus mechanisms used in blockchain technology, and thoroughly analyze anonymity and privacy protection in digital currency. Aiming at data encryption mechanism, we discuss existing anonymity and privacy protection schemes. Our discussion can further promote the development of Blockchain.
Modeling the Screen Content Image Quality via Multiscale Edge Attention Similarity

Screen content image (SCI) prevails because of the explosive growth of screen oriented applications. This leads to extensive studies on SCI quality assessment and modeling for application optimization. In this paper, we propose a full reference multiscale edge attention (MSEA) similarity index to efficiently measure the perceptual quality of a screen image. This model considers the perceptual impacts of fixation attention, edge structure and edge contrast jointly, to accurately capture the masking phenomena (e.g., frequency selectivity, luminance, contrast, etc.) of our human visual system (HVS) when viewing a screen image. Specifically, we decompose the images using Gaussian and Laplacian pyramids which are then used to derive the edge structure, and edge contrast feature maps. Together with the fixation attention map generated by weighted luminance difference between the reference and distorted SCIs, we could eventually offer a MSEA similarity map for final index score. We have evaluated this model using a publicly accessible screen image database. Simulation results have shown that the MSEA similarity index correlates with the collected subjective mean opinion score (MOS) very well. In fact, it is ranked at the first place for both Pearson linear correlation coefficient (PLCC) and Root mean squared error (RMSE), and ranked at the second place for Spearman rank-order correlation coefficient (SROCC) measurements, among existing quality metrics.
Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras

ABSTRACT Glass composition‐based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well‐trained models. Eruptives from Alaska's Aleutian Arc‐Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top‐performing learners (accuracy and kappa &gt; 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south‐central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene‐active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers.
A Method for Eliciting and Representing Emotional Requirements: Two Case Studies in e-Healthcare

This paper describes a method for eliciting and representing emotional requirements for sociotechnical systems in a holistic manner along with eliciting and representing functional and quality requirements. As emotional requirements are crucial in designing sociotechnical systems for e-healthcare, the application of the method in two case studies of this problem domain is described.
A Reputation Management Framework for Knowledge-Based and Probabilistic Blockchains

Recently, leading research communities have been investigating the use of blockchains for Artificial Intelligence (AI) applications, where multiple participants, or agents, collaborate to make consensus decisions. To achieve this, the data in the blockchain storage have to be transformed into blockchain knowledge. We refer to these types of blockchains as knowledge-based blockchains. Knowledge-based blockchains are potentially useful in building efficient risk assessment applications. An earlier work introduced probabilistic blockchain which facilitates knowledge-based blockchains. This paper proposes an extension for the probabilistic blockchain concept. The design of a reputation management framework, suitable for such blockchains, is proposed. The framework has been developed to suit the requirements of a wide range of applications. In particular, we apply it to the detection of malicious nodes and reduce their effect on the probabilistic blockchains' consensus process. We evaluate the framework by comparing it to a baseline using several adversarial strategies. Further, we analyze the collaborative decisions with and without the malicious node detection. Both results show a sustainable performance, where the proposed work outperforms others and achieves excellent results.
A Security Reference Architecture for Blockchains

Due to their specific features, blockchains have become popular in recent years. Blockchains are layered systems where security is a critical factor for their success. The main focus of this work is to systematize knowledge about security and privacy issues of blockchains. To this end, we propose a security reference architecture based on models that demonstrate the stacked hierarchy of various threats as well as threat-risk assessment using ISO/IEC 15408. In contrast to the previous surveys [23], [88], [11], we focus on the categorization of security vulnerabilities based on their origins and using the proposed architecture we present existing prevention and mitigation techniques. The scope of our work mainly covers aspects related to the nature of blockchains, while we mention operational security issues and countermeasures only tangentially.
Weakly Supervised Semantic Segmentation by a Class-Level Multiple Group Cosegmentation and Foreground Fusion Strategy

Weakly supervised semantic segmentation uses image-level labels to extract object regions. The existing methods focus on efficiently training CNN-based segmentation networks using the image-level labels. In contrast to the existing methods, this paper proposes a new fusion-based method, which first segments the foregrounds of each image by multiple group cosegmentation and then generates the semantic segmentation by combining the foregrounds. Specifically, a new CNN-based multiple group cosegmentation network is first proposed to segment foregrounds employing two cues, the discriminative cue and the local-to-global cue. Then, the fusion method is proposed to simply perform semantic segmentation based on the multiple group cosegmentation results. Experiments on the PASCAL VOC 2012 and MS COCO 2017 datasets demonstrate the effectiveness of the proposed method with mIoU values that are obviously larger than those of the existing methods.
Deep Learning for Improving the Robustness of Image Encryption

In this paper, we propose a method to increase the robustness of 2D/3D optical image encryption using the dilated deep convolutional neural network (CNN).In order to solve the problem that encrypted images suffer from some attacks in practical application, we utilize a fast and effective CNN denoiser based on the principle of deep learning.The CNN improves the robustness of the algorithm by improving the resolution of the reconstructed images.Besides, CNN has a high performance against blur and occlusion attacks.We introduce the pixel scrambling method to enhance the security level of the encryption by the private key of pixel scrambling operation.The proposed method can not only realize the encryption of a two-dimensional image but also implement three-dimensional image encryption by combining the integral imaging technology.Double random phase encoding in the fractional Fourier domain is selected for experimental verification, and the results show the capability for robustness, noise immunity, and security of the proposed method.
Learning Spatiotemporal Attention for Egocentric Action Recognition

Recognizing camera wearers' actions from videos captured by the head-mounted camera is a challenging task. Previous methods often utilize attention models to characterize the relevant spatial regions to facilitate egocentric action recognition. Inspired by the recent advances of spatiotemporal feature learning using 3D convolutions, we propose a simple yet efficient module for learning spatiotemporal attention in egocentric videos with human gaze as supervision. Our model employs a two-stream architecture which consists of an appearance-based stream and motion-based stream. Each stream has the spatiotemporal attention module (STAM) to produce an attention map, which helps our model to focus on the relevant spatiotemporal regions of the video for action recognition. The experimental results demonstrate that our model is able to outperform the state-of-the-art methods by a large margin on the standard EGTEA Gaze+ dataset and produce attention maps that are consistent with human gaze.
Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.
The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain Case

The aim of this study is to understand the blockchain adoption behaviour in Brazilian supply chains. Specifically, the study aims to unlock the potential of social influence; taking into consideration workers from the Brazilian supply chains. We proposed a model using constructs from a unified theory of acceptance and the use of technology (UTAUT). The models were analysed using partial least squares structural equation modelling (PLS-SEM) and the findings indicated the power of social influence in predicting other UTAUT constructs. In addition, this study shows the mediation effect of effort expectancy and the facilitating conditions in the relationship between social influence and the behavioural intention to adopt blockchain. Finally, our findings bring essential theoretical and managerial implications.