Reviewerly/exHarmony huggingface dataset:
Oversampling method using outlier detectable generative adversarial network

A class imbalance problem occurs when a particular class of data is significantly more or less than another class of data. This problem is difficult to solve; however, solutions such as the oversampling method using synthetic minority oversampling technique (SMOTE) or conditional generative adversarial network (cGAN) have been suggested recently to solve this problem. In the case of SMOTE and their variations, it is possible to generate biased artificial data because it does not consider the entire data in the minority class. To overcome this problem, an oversampling method using cGAN has been proposed. However, such a method does not consider the majority class that affects the classification boundary. In particular, if there is an outlier in the majority class, the classification boundary may be biased. This paper presents an oversampling method using outlier detectable generative adversarial network (OD-GAN) to solve this problem. We use a discriminator, which is used only for training purposes in cGAN, as an outlier detector to quantify the difference between the distributions of the majority and minority classes. The discriminator can detect and remove outliers. This prevents the distortion of the classification boundary caused by outliers. The generator imitates the distribution of the minority class and generates artificial data to balance the dataset. We experiment with various datasets, oversampling techniques, and classifiers. The empirical results show that the performance of OD-GAN is better than those of other oversampling methods for imbalanced datasets with outliers.
An Empirical Study on Generalizations of the ReLU Activation Function

Deep Neural Networks have become the tool of choice for Machine Learning practitioners today. They have been successfully applied for solving a large class of learning problems both in the industry and academia with applications in fields such as Computer Vision, Natural Language Processing, Big data Analytics and Bioinformatics. One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. Activation functions are used for introducing non-linearity into the neural network model so that the network can progressively learn more effective feature representations. Several different activation functions have been used in the literature. However Linear, Sigmoid, Tanh and ReLU are the most commonly used activation functions and they are often selected empirically during the network design phase, rather than through a proper data driven process. In this work we empirically study the problem of generalizing the single output ReLU activation by parameterizing the same so that data driven methods can be used to select variations of the single output ReLU. We call this class of activations the Generalized ReLU Activations. Special cases are ReLU as well as variations like the Leaky ReLU that have already been studied in the literature. We report results of extensive experiments on the well known MNIST handwriting dataset.
A large-scale empirical exploration on refactoring activities in open source software projects

Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this paper, we continue on this line of research by performing a large-scale empirical analysis of refactoring practices in 200 open source systems. Specifically, we analyze the change history of these systems at commit level to investigate: (i) whether developers perform refactoring operations and, if so, which are more diffused and (ii) when refactoring operations are applied, and (iii) which are the main developer-oriented factors leading to refactoring. Based on our results, future research can focus on enabling automatic support for less frequent refactorings and on recommending refactorings based on the developer's workload, project's maturity and developer's commitment to the project.
Consumer-facing technology fraud: Economics, attack methods and potential solutions

The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives — we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks.
An evaluation of methodologies for calibrating Itrax X-ray fluorescence counts with ICP-MS concentration data for discrete sediment samples

Core-scanning X-ray Fluorescence (XRF-CS) is a well-established technique for rapid (<30 s/interval) analysis of sediment core geochemistry at sub-mm resolution with substantially less analytical cost compared to methods that rely on physical sub-sampling. Due to issues inherent in analyzing wet sediment of heterogeneous particle size and composition with irregular surface topography using XRF, XRF-CS results are generally considered semi-quantitative. The result of early efforts to calibrate XRF-CS data with conventional geochemical results (e.g. WD- or ED-XRF, ICP-AES, ICP-MS) showed weak correlations for less abundant or poorly detectable elements, however, more recent methods have been proposed to improve accuracy. These methods include: 1) converting XRF-CS results to dry mass concentration; 2) normalizing XRF-CS data to conservative elements (Si, Ca), total counts/second, or X-ray scatter (CIR); and 3) calibration of data using multivariate analysis of elemental log-ratios (MLC). These approaches are not yet widely employed, and require additional testing on a variety of sediment compositions. Recently developed equipment enables analysis of discrete sediment samples, providing >30 replicate analyses for up to 180 samples in a single XRF-CS run. These replicate measurements allow for rigorous testing of precision and accuracy of XRF-CS data. To determine the ideal method of data transformation to improve XRF-CS calibration to quantitative geochemical concentration, 100 lake sediment-surface samples collected from Harvey Lake, New Brunswick, Canada, were analyzed using Itrax-XRF-CS, and then with ICP-MS analysis after multi-acid digestion. Normalization using the CIR and correction for water content showed strong correlation coefficients (Kendall's τ) for elements with atomic number > 18 and high concentrations in the sediment. Results for lighter elements and those with lower concentrations did not perform well using these calibration methods. The MLC provided the most accurate reproduction of observed ICP-MS trends and strong correlations (R) between predicted and actual geochemical concentrations. Based on these results, CIR-normalized or wet-corrected calibrations are ideal for studies where absolute geochemical values are of lesser importance, and the MLC method is appropriate for studies with large numbers of sediment samples (n > 100), or those where absolute concentrations of elements are of greater importance.
Quantum Circuit Designs of Integer Division Optimizing T-count and T-depth

Quantum circuits for mathematical functions such as division are necessary to use quantum computers for scientific computing. Quantum circuits based on Clifford+T gates can easily be made fault-tolerant but the T gate is very costly to implement. The small number of qubits available in existing quantum computers adds another constraint on quantum circuits. As a result, reducing T-count and qubit cost have become important optimization goals. The design of quantum circuits for integer division has caught the attention of researchers and designs have been proposed in the literature. However, these designs suffer from excessive T gate and qubit costs. Many of these designs also produce significant garbage output resulting in additional qubit and T gate costs to eliminate these outputs. In this work, we propose two quantum integer division circuits. The first proposed quantum integer division circuit is based on the restoring division algorithm and the second proposed design implements the non-restoring division algorithm. Both proposed designs are optimized in terms of T-count, T-depth and qubits. Both proposed quantum circuit designs are based on (i) a quantum subtractor, (ii) a quantum adder-subtractor circuit, and (iii) a novel quantum conditional addition circuit. Our proposed restoring division circuit achieves average T-count savings from 79.03 to 91.69 percent compared to the existing works. Our proposed non-restoring division circuit achieves average T-count savings from 49.22 to 90.03 percent compared to the existing works. Further, both our proposed designs have linear T-depth. We also illustrate the application of the proposed quantum division circuits in quantum image processing with a case study of quantum bilinear interpolation.
Draw It As Shown: Behavioral Pattern Lock for Mobile User Authentication

Android pattern lock is still popularly used for mobile user authentication. Unfortunately, however, many concerns have been raised regarding its security and usability. User-created patterns tend to be simply structured or reduced to a small set. Complex patterns are hard to memorize. Input patterns are susceptible to various attacks, such as guessing attacks, smudge attacks, and shoulder surfing attacks. This paper presents a novel mechanism based on the pattern lock, in which behavioral biometrics are employed to address these problems. Our basic idea starts from turning the lock pattern into public knowledge rather than a secret and leveraging touch dynamics. Users do not need to create their own lock patterns or memorize them. Instead, our system shows a public pattern along with guidance on how to draw it. All the user needs to do for authentication is to draw the pattern as shown. For adversaries, the above-mentioned attacks are rendered useless by this new mechanism. Specifically, we study how to generate the public patterns and how to perform authentication. We considered segments, angles, directions, and turns as units for constructing the lock patterns, and established the public pattern criteria. The results are utilized to generate four public patterns in our experiment. For authentication, we achieved equal error rates (EERs) as low as 2.66% (sitting), 3.53% (walking), and 5.83% (combined). Furthermore, the results of our additional experiments demonstrated that our system preserved performance over time (F1-score = 89.88%, SD = 4.60%), and was sufficiently secure against camera-based recording attacks (FAR = 3.25%).
Hierarchical Weakly Supervised Learning for Residential Area Semantic Segmentation in Remote Sensing Images

Residential-area segmentation is one of the most fundamental tasks in the field of remote sensing. Recently, fully supervised convolutional neural network (CNN)-based methods have shown superiority in the field of semantic segmentation. However, a serious problem for those CNN-based methods is that pixel-level annotations are expensive and laborious. In this study, a novel hierarchical weakly supervised learning (HWSL) method is proposed to realize pixel-level semantic segmentation in remote sensing images. First, a weakly supervised hierarchical saliency analysis is proposed to capture a sequence of class-specific hierarchical saliency maps by computing the gradient maps with respect to the middle layers of the CNN. Then, superpixels and low-rank matrix recovery are introduced to highlight the common salient areas and fuse class-specific saliency maps with adaptive weights. Finally, a subtraction operation between class-specific saliency maps is conducted to generate hierarchical residual saliency maps and fulfill residential-area segmentation. Comprehensive evaluations with two remote sensing data sets and comparison with seven methods validate the superiority of the proposed HWSL model.
Insecure connection bootstrapping in cellular networks

In the cellular ecosystem, base stations act as trusted intermediaries between cellular devices and the core network. During connection bootstrapping, devices currently, however, do not possess any mechanisms to authenticate a base station before connecting to it. This lack of authentication has been shown to be exploitable by adversaries to install fake base stations which can lure unsuspecting devices to connect to them and then launch sophisticated attacks. Despite being a well-known threat to the cellular ecosystem, this weakness is not addressed in the current protocol versions including 5G. The current paper sets out to fill this void by proposing a Public-key infrastructure (PKI) based authentication mechanism which builds on top of the asymmetric cryptography used in 5G and adheres to the relevant deployment constraints. Our proposed authentication scheme leverages precomputation-based digital signature generation algorithms and employs optimizations in three dimensions---PKI scheme-level, protocol-level, and cryptographic scheme-level---to address the trilemma of small signature size, efficient signature generation, and short verification time. Our evaluation on a real testbed indicates that the proposed scheme is not only readily deployable but also performs better than a symmetric key-based scheme (i.e., TESLA) in terms of security guarantee, overhead, and deployment constraints (e.g., backward compatibility).
Hybrid Harmony Search Algorithm With Grey Wolf Optimizer and Modified Opposition-Based Learning

Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection.Many variants have been developed to cope with this problem and improve algorithm performance.In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection.Then, a modified version of opposition-based learning technique has been applied to the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima.Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth.The proposed hybrid algorithm for global optimization problems is called GWO-HS.The GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014.Then, the GWO-HS has been compared with recent HS variants and other wellknown metaheuristic algorithms.The results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.
Decentralised Image Sharing and Copyright Protection using Blockchain and Perceptual Hashes

In this paper, we aim to address the major problem of correct image attribution for photos being uploaded on the stock photo websites and peer-to-peer image sharing marketplaces, ensuring that original photographers are rightfully recognized and credited for their works. There are now millions of royalty-free images being shared every week on stock photo websites. There is a major discontent among photographers and graphics designers who share images on such websites regarding a widespread malpractice where others try to take credit for and benefit from original author's work. This happens when someone other than the original author uploads the same image as one already shared on the marketplace or uses image processing methods such as rotation, cropping, gray scale conversion etc and then uploads this tampered image to avoid copyright violation detection. To counter such problems, we propose a decentralised peer-to-peer photo sharing marketplace built on top of Ethereum test chain and demonstrate how it is fair, trustworthy and practical. Our decentralised application leverages perceptual hashes and robust smart contracts of Ethereum to automatically detect and reject tampered images that are perceptually similar to images already present on the marketplace. Due to inherent properties of Blockchain, our marketplace has no central authority controlling it, no third party interference, no single point of failure, zero-censorship and preserves online user privacy. To the best of our knowledge, this is the first work that provides a practical solution for automatically detecting and rejecting perceptually similar images on a decentralised image sharing platform, thus protecting the copyrights of genuine image authors.
Investigating the Third Dimension for Authentication in Immersive Virtual Reality and in the Real World

Immersive Virtual Reality (IVR) is a growing 3D environment, where social and commercial applications will require user authentication. Similarly, smart homes in the real world (RW), offer an opportunity to authenticate in the third dimension. For both environments, there is a gap in understanding which elements of the third dimension can be leveraged to improve usability and security of authentication. In particular, investigating transferability of findings between these environments would help towards understanding how rapid prototyping of authentication concepts can be achieved in this context. We identify key elements from prior research that are promising for authentication in the third dimension. Based on these, we propose a concept in which users' authenticate by selecting a series of 3D objects in a room using a pointer. We created a virtual 3D replica of a real world room, which we leverage to evaluate and compare the factors that impact the usability and security of authentication in IVR and RW. In particular, we investigate the influence of randomized user and object positions, in a series of user studies (N=48). We also evaluate shoulder surfing by real world bystanders for IVR (N=75). Our results show that 3D passwords within our concept are resistant against shoulder surfing attacks. Interactions are faster in RW compared to IVR, yet workload is comparable.
Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning

Occlusion as a core challenge for stereo computation has attracted extensive research efforts in the past decades. Apart from its adverse impact, occlusion itself is a crucial clue which has not been exploited in the field of CNN based stereo. In this paper, we argue that a deep stereo framework benefits from reasoning occlusion in advance. We present an occlusion aware stereo network comprising a prior occlusion inferring module and a subsequent disparity computation module. The occlusion inferring module is a sub-network that directly starts from images, which averts the sophisticated procedure to iteratively estimate occlusion with disparity. We additionally propose cooperative unsupervised learning of occlusion and disparity, based on a different hybrid loss enforcing them to be consensus and trained alternatively to reach convergence. The comprehensive experimental analyses show that our method achieves state-of-the-art results among unsupervised learning frameworks, and is even comparable to several supervised methods.
Inequality and the economic participation of women in sub-Saharan Africa

Purpose The purpose of this paper is to investigate the effect of inequality on female employment in 42 countries in sub-Saharan Africa (SSA) for the period 2004–2014. Design/methodology/approach Three inequality indicators are used, namely, the: Gini coefficient, Atkinson index and Palma ratio. Two indicators of gender inclusion are also employed, namely: female employment and female unemployment rates. The empirical analysis is based on the generalised method of moments. Findings The following main findings are established. First, inequality increases female unemployment in regressions based on the Palma ratio. Second, from the robustness checks, inequality reduces female employment within the frameworks of the Gini coefficient and Palma ratio. Originality/value Studies on the relevance of income inequality on female economic participation in SSA are sparse.
Capabilities of ARCore and ARKit Platforms for AR/VR Applications

In this paper ARCore and ARkit capabilities were scrutinized and compared. Authors established comparison criteria for both platforms, developed test applications and ran comparison tests. Obtained results can be a help in choosing the right framework to speed up prototyping and development of modern AR/VR applications. This work consists of a comprehensive comparison of these new frameworks in the following respects: general performance (CPU/memory use), mapping of planes on various surface types, influence of light and movement on mapping quality etc.
Exploiting Discriminative Regions of Brain Slices Based on 2D CNNs for Alzheimer’s Disease Classification

Convolutional neural networks (CNNs)-based classifiers improve the accuracy of diagnosis and prediction for Alzheimer's disease (AD). However, exploiting specific brain regions with the AD is essential to understand pathological alteration in the AD and monitor its progression. This paper aims to construct novel AD classification models which have a good performance and interpretation on AD diagnosis. We propose the three classifiers including a simple broaden plain CNNs (SBPCNNs), a major slice-assemble CNNs (SACNNs) and a multi-slice CNNs (MSCNNs), which record the slice positions but have fewer parameters. Specifically, we integrate the ranking and the random forest methods to find the discriminative region that is consistent with domain knowledge about the AD. The results of the visualization explanation of pixel and slice level deliver a clearer understanding of the AD to specialists. The experimental results indicate that the proposed models are meaningful for AD classification.
Coalition Game-Based Computation Resource Allocation for Wireless Blockchain Networks

Public blockchain network (PBN) has been widely used in wired networks such as bitcoin network, in which proof-of-work (PoW) algorithm is deployed among miners to reach consensus on users data during the mining process. However, the PoW consensus mechanism is computation-consuming which obstacles the application of PBN in wireless mobile networks since most Internet of Things/mobile devices (IMDs) are resource limited. Recently, mobile edge computing (MEC) has been regarded as a promising technology which can allow IMDs to offload their computation tasks to the edge nodes. Although IMDs can offload their computation tasks to the edge nodes, there is still lots of competition among enormous solo mining IMDs when reaching consensus. In this paper, we first formulate the computation resource allocation problem of PBN from the viewpoint of coalition game theory under the MEC environment. Then, we propose a coalition formation game-based algorithm to maximize the system sum utility and take both the individual profit of IMD and coalition profit into consideration. Furthermore, we prove the proposed algorithm converges to a Nash-stable partition in a fast convergence rate and finally reaches the near-optimal solution with low computational complexity. The simulation results demonstrate the optimality and convergence of the proposed algorithm, and the proposed algorithm outperforms other schemes in terms of system sum profit and ratio of rewarded IMDs to overall IMDs.