Reviewerly/exHarmony huggingface dataset:
Improving phase estimation using number-conserving operations

We propose a theoretical scheme to improve the resolution and precision of phase measurement with parity detection in the Mach-Zehnder interferometer by using a nonclassical input state which is generated by applying a number-conserving generalized superposition of products (GSP) operation, (saa^{{\dag}}+ta^{{\dag}}a)^{m} with s^2+t^2=1, on two-mode squeezed vacuum (TMSV) state. The nonclassical properties of the proposed GSP-TMSV are investigated via average photon number (APN), anti-bunching effect, and degrees of two-mode squeezing. Particularly, our results show that both higher-order m GSP operation and smaller parameter s can increase the total APN, which leads to the improvement of quantum Fisher information. In addition, we also compare the phase measurement precision with and without photon losses between our scheme and the previous photon subtraction/addition schemes. It is found that our scheme, especially for the case of s=0, has the best performance via the enhanced phase resolution and sensitivity when comparing to those previous schemes even in the presence of photon losses. Interestingly, without losses, the standard quantum-noise limit (SQL) can always be surpassed in our our scheme and the Heisenberg limit (HL) can be even achieved when s=0.5,1 with small total APNs. However, in the presence of photon losses, the HL cannot be beaten, but the SQL can still be overcome particularly in the large total APN regimes. Our results here can find important applications in quantum metrology.
Efficient and secure substitution box and random number generators over Mordell elliptic curves

Elliptic curve cryptography has received great attention in recent years due to its high resistance against modern cryptanalysis. The aim of this article is to present efficient generators to generate substitution boxes (S-boxes) and pseudo random numbers which are essential for many well-known cryptosystems. These generators are based on a special class of ordered Mordell elliptic curves. Rigorous analyses are performed to test the security strength of the proposed generators. For a given prime, the experimental results reveal that the proposed generators are capable of generating a large number of distinct, mutually uncorrelated, cryptographically strong S-boxes and sequences of random numbers in low time and space complexity. Furthermore, it is evident from the comparison that the proposed schemes can efficiently generate secure S-boxes and random numbers as compared to some of the well-known existing schemes over different mathematical structures.
Greedy auto-augmentation for n-shot learning using deep neural networks

The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmentation allow us to produce an infinite number of target conditions from the primary condition. This process includes two main steps for finding the best augmentations and training the data with the new augmentation techniques. Optimizing these two steps for n-shot learning is still an open problem. In this paper, we propose a new method for auto-augmentation to address both of these problems. The proposed method can potentially extract many possible types of information from a small number of available data points in n-shot learning. The results of our experiments on five prominent n-shot learning datasets show the effectiveness of the proposed method.
Performance Evaluation and Practical Use of Supervised Data Mining Algorithms for Credit Card Approval

Business analysis is using large volume of data to help people to make decision. Supervised learning methods are widely used in bank system, especially in credit card approval. The goal is to use various data mining algorithms to predict whether a new customer credit scores or to detect credit fraud. This will serve as the basis for the design of a new campaign because a transition from manual review to automatic review by computer could dramatically improve the work efficiency and reduce the labor costs. More tools such as self organizing map and algorithms could shed light on the self-evaluation for credit card applicants, which could raise efficiency.
Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes

Abstract This paper outlines a new framework for the calibration of optical instruments, in particular smartphone cameras, using highly redundant circular black‐and‐white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centres; and (iii) iteratively improving the calibration solution through a free‐network self‐calibrating bundle adjustment. The proposed method effectively matched circular targets in 270 smartphone images, taken within a calibration laboratory, with robustness to type II errors (false negatives). The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved comparably to available closed‐form solutions, which require additional a priori object‐space target information. Finally, specifically for the case of mobile devices, the calibration parameters obtained using the framework were found to be superior compared to in situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45 % improvement on average).
Bias-Corrected Intuitionistic Fuzzy C-Means With Spatial Neighborhood Information Approach for Human Brain MRI Image Segmentation

Segmentationof MRI imagesin the presence of artifacts such as noise and bias field effect (BFE) is challenging. In this article, to handle both artifacts, we have formulated an intuitionistic fuzzy set (IFS) theory based bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information (SNI) method for magnetic resonance imaging (MRI) image segmentation. We represent the image in terms of IFSs using two well-known intuitionistic fuzzy generation functions, namely Sugeno’s negation function (SNF) and Yager’s negation function (YNF). In this way, the proposed approach takes advantage of the IFS theory to address the uncertainty and vagueness present in data. The SNI term helps to retain the fine image details in the segmentation process, which is usually lost when smoothing is used. The BFE is modeled as a slow varying intensity field additive in nature with mean zero in IFS framework. The proposed approach estimates the bias field and produces a segmented image simultaneously. The segmentation performance of the proposed method is evaluated in terms of the Dice score, average segmentation accuracy (ASA), and index of variation. Further, the comparison of the proposed method with other similar state-of-the-art methods on two well-known publicly available Brain MRI datasets shows the significant improvements in segmentation performance in terms of ASA, Dice score, and index of variation. The proposed approach achieves 93% ASA in the presence of severe bias field and noise artifacts on BrainWeb simulated dataset, which outperformed the state-of-the-art methods.
ESync: Accelerating Intra-Domain Federated Learning in Heterogeneous Data Centers

Federated Learning (FL) serves privacy-preserving collaborative learning among multiple isolated parties, while retaining their privacy data locally. Cross-device and cross-silo FL have achieved great success in cross-domain applications, in which the scarce communication resource is the primary bottleneck. Driven by the need to combine heterogeneous machines from different parties to build a shared data center, we found <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra-domain FL</i> , a new type of FL in which isolated parties collaborate in the shared data center, and strong computational heterogeneity becomes the primary bottleneck. To mitigate the training inefficiency caused by stragglers, this article proposes an efficient synchronization algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESync</i> , which allows parties to train different iterations locally under the coordination of a novel scheduler <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">State Server</i> . We give the boundaries of weight divergence and optimality gap of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESync</i> , and analyze the trade-off between convergence accuracy and communication efficiency. Extensive experiments are conducted to compare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESync</i> with SSGD, ASGD, DC-ASGD, FedAvg, FedAsync, TiFL, and FedDrop under strong computational heterogeneity. Numerical results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESync</i> achieves great speed up without loss of accuracy, and therefore demonstrate the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ESync</i> in both training efficiency and converged accuracy.
Aesthetic assessment of website design based on multimodal fusion

As a medium of high-frequency access, websites highly affect our life. Due to the specific and subjective content selection criteria of viewers, they prefer to click on pages that they think look comfortable to them. Some viewers prefer better UI design whereas others prefer ease of access where they do not have to spend much time on the website to find what they are looking for. Therefore, designing web pages which attracts the attention of Internet users helps increasing the number of views which in turn results in increased economic benefits. A reasonable aesthetic assessment of a web design has become an important research topic. Since the aesthetic assessment of web design has great subjectivity, it becomes very difficult to correctly evaluate the web page aesthetics. For web page assessment, this paper divides the task into multiple modalities. In this paper, multiple modes are evaluated separately, and a more accurate assessment of each mode is presented. Assessments of the various modalities are combined to get an overall eigen vector. A complex and subjective assessment task is decomposed into multimodal subtasks and a more specific and accurate feature vector is obtained by merging the subtask assessments. This method can solve the complex problem of subjective assessment tasks which, otherwise, are difficult to solve directly through training model. By conducting several subjective experiments and comparing the outcome with the results predicted by the model, it is shown that the proposed model is correlated well to the subjective measurement of the viewer and provide a strong reference for the aesthetic assessment of a web page.
Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation

Machine learning service API allows model owners to monetize proprietary models by offering prediction services to third-party users.However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate prediction queries and their responses to train a replica model.As countermeasures, researchers have proposed to reduce the rich API output, such as hiding the precise confidence.Nonetheless, even with response being only one bit, an adversary can still exploit fine-tuned queries with differential property to infer the decision boundary of the underlying model.In this article, we propose boundary differential privacy (BDP) against such attacks by obfuscating the prediction responses with noises.BDP guarantees an adversary cannot learn the decision boundary of any two classes by a predefined precision no matter how many queries are issued to the prediction API.We first design a perturbation algorithm called boundary randomized response for a binary model.Then we prove it satisfies -BDP, followed by a generalization of this algorithm to a multiclass model.Finally, we generalize a hard boundary to soft boundary and design an adaptive perturbation algorithm that can still work in the latter case.The effectiveness and high utility of our solution are verified by extensive experiments on both linear and non-linear models.
Direct and Indirect Estimation of the Variance–Covariance Matrix of the Parameters of a Fitted Ellipse and a Triaxial Ellipsoid

This work deals with the estimation of the variance–covariance matrix of the parameters of a fitted ellipse and an ellipsoid by a direct and an indirect procedure. In the direct approach, the Cartesian equation of an ellipsoid was expressed in terms of the coordinates of the ellipsoid center, the three ellipsoid semiaxes, and the three rotation angles. The general least-squares method was applied to estimate these parameters and their variance–covariance matrix. In the indirect approach, the Cartesian equation of an ellipsoid was expressed as a polynomial. The coefficients of this polynomial equation and their variance–covariance matrix were estimated using the general least-squares method. Then these coefficients were transformed into the parameters of the ellipsoid through an analytical diagonalization of a suitable matrix. The variance–covariance matrix of these parameters was estimated applying the law of propagation of variances. Both approaches are applied to the special case of an ellipse. The numerical examples in both cases indicated that the two procedures produce almost identical results.
Mosquito Classification Using Convolutional Neural Network with Data Augmentation

Mosquitoes are responsible for the most number of deaths every year throughout the world. Bangladesh is also a big sufferer of this problem. Dengue, malaria, chikungunya, zika, yellow fever etc. are caused by dangerous mosquito bites. The main three types of mosquitoes which are found in Bangladesh are aedes, anopheles and culex. Their identification is crucial to take the necessary steps to kill them in an area. Hence, a convolutional neural network (CNN) model is developed so that the mosquitoes could be classified from their images. We prepared a local dataset consisting of 442 images, collected from various sources. An accuracy of 70% has been achieved by running the proposed CNN model on the collected dataset. However, after augmentation of this dataset which becomes 3,600 images, the accuracy increases to 93%. We also showed the comparison of some methods with the CNN method which are VGG-16, Random Forest, XGboost and SVM. Our proposed CNN method outperforms these methods in terms of the classification accuracy of the mosquitoes. Thus, this research forms an example of humanitarian technology, where data science can be used to support mosquito classification, enabling the treatment of various mosquito borne diseases.
Enhancing First-Person View Task Instruction Videos with Augmented Reality Cues

This research investigates enhancing first-person view (FPV) task instruction videos by applying Augmented Reality (AR) visualisation of spatial cues. With personal mobile devices, recording and sharing a video clip has become very easy, and how-to videos are becoming popular on social video sharing services. Instructional videos are actively used not only in formal education and training, but also in everyday life. However, video clips are limited to two-dimensional representation of the task space, making it hard for the viewer to follow and match the objects in the video to those in the real world task space. We propose augmenting task instruction videos with AR visualisation of spatial cues to overcome this problem, focusing on creating and viewing FPV instruction videos. We designed and implemented a prototype system, AR Tips, which allows users to capture and share augmented FPV instruction videos on a wearable AR device. We conducted a user study to evaluate the benefit of our approach, and the results showed that with the help of augmented spatial cues users better understood the instructions, performed the tasks faster with fewer errors, and had lower mental effort.
Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

Abstract Autonomous obstacle avoidance control of unmanned surface vessels (USVs) in complex marine environments is always fundamental for its scientific search and detection. Traditional methods usually model USV motion and environments in a mathematical way that needs perceptual information. Unfortunately, it is difficult to provide sufficient perceptual information due to complex marine environments, resulting in inaccurate modeling. Reinforcement learning has recently enjoyed increasing popularity in the problem of obstacle avoidance since it can settle problems by partially observable environment information. However, the autonomous USV obstacle avoidance using reinforcement learning is still facing the challenge of designing appropriate reward functions under complex marine environments. To address these issues, we propose a prior knowledge‐based USV reinforcement learning obstacle avoidance algorithm. In this algorithm, an actor‐critic network is used as the main architecture of the algorithm, and prior knowledge‐based reward shaping used to design relevant reward function for USV obstacle avoidance. A standard USV based on a visual sensor is designed, and the state input of the algorithm is through USV's front vision sensor. We conducted simulation experiments and results prove that our algorithm can effectively converge, and USV achieves high velocity and low collision rate in the complex marine environment.
On the Adversarial Robustness of Hypothesis Testing

In this paper, we investigate the adversarial robustness of hypothesis testing rules. In the considered model, after a sample is generated, it will be modified by an adversary before being observed by the decision maker. The decision maker needs to decide the underlying hypothesis that generates the sample from the adversarially-modified data. We formulate this problem as a minimax hypothesis testing problem, in which the goal of the adversary is to design attack strategy to maximize the error probability while the decision maker aims to design decision rules so as to minimize the error probability. We consider both hypothesis-aware case, in which the attacker knows the true underlying hypothesis, and hypothesis-unaware case, in which the attacker does not know the true underlying hypothesis. We solve this minimax problem and characterize the corresponding optimal strategies for both cases.
Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology, the Internet of Things (IoT) has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people's production and life.However, the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay, single point of failure, privacy disclosure and other problems, and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project, this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible, but also can better protect the data security of the Internet of Things.
Gendered Violence Online: Hate Speech as an Intersection of Misogyny and Racism

Social media has been adopted by radical right populists and alt-right demagogues as a platform for circulating misogynous and racist hate speech and affectively mobilising supporters. This chapter examines hate speech as a form of gendered and racist violence, focusing on social media posts by two influential right-wing populist politicians: Jussi Halla-aho, the leader of the Finns Party, and Donald Trump, the president of the United States. It demonstrates how these politicians intertwine misogyny and racism in blog posts and tweets that target women in particular. Their affective online communication is interpreted as a new form of violence. This digitally mediated violence, although a relatively new phenomenon, is an integral part of the chain of violence that links interpersonal violence, violent societal structures and ideological and political violence. Discursive expressions of hatred are spread through social media and shape our understanding of reality. They are not only violent themselves but also pave the way for an ideological readiness to use other types of violence.
Expected-Time Cryptography: Generic Techniques and Applications to Concrete Soundness

This paper studies concrete security with respect to expected-time adversaries. Our first contribution is a set of generic tools to obtain tight bounds on the advantage of an adversary with expected-time guarantees. We apply these tools to derive bounds in the random-oracle and generic-group models, which we show to be tight. As our second contribution, we use these results to derive concrete bounds on the soundness of public-coin proofs and arguments of knowledge. Under the lens of concrete security, we revisit a paradigm by Bootle et al. (EUROCRYPT '16) that proposes a general Forking Lemma for multi-round protocols which implements a rewinding strategy with expected-time guarantees. We give a tighter analysis, as well as a modular statement. We adopt this to obtain the first quantitative bounds on the soundness of Bulletproofs (Bünz et al., S&P 2018), which we instantiate with our expected-time generic-group analysis to surface inherent dependence between the concrete security and the statement to be proved.
Defining cyber risk

Rapid digitization of the economy and social relations is the main reason why the issues of cyber risk, cyber threats and cybersecurity are continually gaining importance. Despite the increase in the number of research papers in these areas, scholarly articles defining cyber risk are relatively scarce. Moreover, the uniform broadly accepted definition of cyber risk has not been adopted yet, probably due to the interdisciplinary nature of this concept and the dynamics of its change. The paper contributes to the literature on the cyber risk, cybersecurity and cyber risk management. The author presents a comparative content analysis of existing definitions of cyber risk. Based on identification of three key characteristics of the cyber risk concept (source of cyber risk, cyber risk object, impact of cyber risk) in each definition, the analysed definitions are categorised as one-dimensional, two-dimensional or comprehensive definition. Among the collected 20 definitions of cyber risk, there is only one that can be called comprehensive. The remaining definitions address only selected aspects of this notion. The author proposes a new, comprehensive and universal definition of cyber risk. As an extension to the proposed approach, the ontological meta model of the cyber risk concept is developed. It supports deeper description of the cyber risk concept by depicting functional interdependencies with other terms and factors that constitute the cyber risk framework.