Reviewerly/exHarmony huggingface dataset:
The Stumbling Balance between Public Health and Privacy amid the Pandemic in China

Abstract In China, to fight against COVID-19, a variety of high- and low-tech methods have been created and applied to find and monitor people confirmed or suspected to have COVID-19 and their close contacts. Such mass monitoring may have the effect of containing and managing the pandemic, but it has brought about prevalent concerns of privacy. Undoubtedly, China is confronted with fulfilling the task of public health surveillance while, at the same time, protecting personal privacy. However, there are three factors that have caused these balancing efforts to falter. The first factor is the lack of strong and solid legal foundations to authorize and control the surveillance while giving sufficient support for personal privacy. The second factor is the huge social mobilization that entails the involvement of a large number of different entities and individuals in the mass surveillance, for whom the general principles of privacy protection proposed by public authorities and academia may not be well understood and/or may be difficult to follow in practice. The third factor is the collectivist thinking and obedience to, and in many cases reverence for, the authority that is deeply entrenched in Chinese culture and easily overwhelms the individualism-oriented modern privacy culture. To get rid of these handicaps in a short time and, hence, to strengthen privacy would be an extravagant hope. However, a landmark piece of legislation on privacy and personal information protection and other supporting regulations in the near future are what we need most. In any event, the current surveillance should not be a ‘new normal’.
An Improved Adaptive Weighted Mean Filtering Approach for Metallographic Image Processing

Abstract Background As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method. Methods The method used to detect the pulse noise points in the image, and then the modified mean method used to filter out the detected noise points. Patents on metallographic image processing have discussed for the development of the proposed methodology. Results It is shown that filter window can be filtered in comparison with the conventional 3×3, 5×5 and 7×7 filt window to reduce noise detection and reduce the complexity of the weight calculation. Conclusion It can be concluded that this method can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image.
A new approach to design S-box generation algorithm based on genetic algorithm

Substitution box (S-box) is one of the most important structures used for byte change operation in block encryption algorithms. An S-box structure with strong cryptological properties makes the enc...
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is heterogeneity, which resides in the various hardware specifications and dynamic states across the participating devices. Theoretically, heterogeneity can exert a huge influence on the FL training process, e.g., causing a device unavailable for training or unable to upload its model updates. Unfortunately, these impacts have never been systematically studied and quantified in existing FL literature. In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. We collect large-scale data from 136k smartphones that can faithfully reflect heterogeneity in real-world settings. We also build a heterogeneity-aware FL platform that complies with the standard FL protocol but with heterogeneity in consideration. Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings. Results show that heterogeneity causes non-trivial performance degradation in FL, including up to 9.2% accuracy drop, 2.32x lengthened training time, and undermined fairness. Furthermore, we analyze potential impact factors and find that device failure and participant bias are two potential factors for performance degradation. Our study provides insightful implications for FL practitioners. On the one hand, our findings suggest that FL algorithm designers consider necessary heterogeneity during the evaluation. On the other hand, our findings urge system providers to design specific mechanisms to mitigate the impacts of heterogeneity.
The Relation of Test-Related Factors to Software Quality: A Case Study on Apache Systems

Abstract Testing represents a crucial activity to ensure software quality. Recent studies have shown that test-related factors (e.g., code coverage) can be reliable predictors of software code quality, as measured by post-release defects. While these studies provided initial compelling evidence on the relation between tests and post-release defects, they considered different test-related factors separately: as a consequence, there is still a lack of knowledge of whether these factors are still good predictors when considering all together. In this paper, we propose a comprehensive case study on how test-related factors relate to production code quality in Apache systems. We first investigated how the presence of tests relates to post-release defects; then, we analyzed the role played by the test-related factors previously shown as significantly related to post-release defects. The key findings of the study show that, when controlling for other metrics (e.g., size of the production class), test-related factors have a limited connection to post-release defects.
3d Unsupervised Kidney Graft Segmentation Based On Deep Learning And Multi-Sequence Mri

Image segmentation is one of the most popular problems in medical image analysis. Recently, with the success of deep neural networks, these powerful methods provide state of the art performance on various segmentation tasks. However, one of the main challenges relies on the high number of annotations that they need to be trained, which is crucial in medical applications. In this paper, we propose an unsupervised method based on deep learning for the segmentation of kidney grafts. Our method is composed of two different stages, the detection of the area of interest and the segmentation model that is able, through an iterative process, to provide accurate kidney draft segmentation without the need for annotations. The proposed framework works in the 3D space to explore all the available information and extract meaningful representations from Dynamic Contrast-Enhanced and T2 MRI sequences. Our method reports a dice of 89.8±3.1%, Hausdorff distance at percentile 95% of 5.8±0.4lmm and percentage of kidney volume difference of 5.9±5.7% on a test dataset of 29 patients subject to a kidney transplant.
Measurement outcomes that do not occur and their role in entanglement transformations

Abstract The characterization of transformations among entangled pure states via local operations assisted by classical communication (LOCC) is a crucial problem in quantum information theory for both theoretical and practical reasons. As LOCC has a highly intricate structure, sometimes the larger set of separable (SEP) maps is considered, which has a mathematically much simpler description. In the literature, mainly SEP maps consisting of invertible Kraus operators have been taken into account. In this paper we show that the consideration of those maps is not sufficient when deciding whether a state can be mapped to another via general SEP transformations. This is done by providing explicit examples of transformations among pure three- and five-qubit states, which are feasible via SEP maps containing singular Kraus operators, however, not possible via SEP maps containing solely regular Kraus operators. The key point that allows to construct the SEP maps is to introduce projective measurements that occur with probability zero on the input state. The fact that it is not sufficient to consider SEP maps composed out of regular Kraus operators even in the case of pure state transformations, also affects the results on LOCC transformations among pure states. However, we show that non-invertible Kraus operators do not help in state transformations under LOCC with finitely many rounds of classical communication, i.e. the necessary and sufficient condition for SEP transformations with invertible Kraus operators is still a necessary condition for convertibility under finite-round LOCC. Moreover, we show that the results on transformations via SEP that are not possible with LOCC (including infinitely many rounds of classical communication) presented in Hebenstreit et al 2016 Phys. Rev. A 93 , 012339 are not affected.
Three Stream Network Model for Lung Cancer Classification in the CT Images

Abstract Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.
The Challenge of the Effective Implementation of FAIR Principles in Biomedical Research

The vision of implementing FAIR (findable, accessible, interoperable, and reusable) principles in health research over the next years includes a vast, open community of health research institutions and researchers fully engaged in enhancing their knowledge-based economy and their research excellence by adopting the FAIR data guiding principles. We envision high-quality health research and routine care data to be shared and reused in a secure, controlled, and legally compliant environment in both human and machine-readable formats. The idea is to accelerate knowledge discovery while reducing biases and enhancing the strength and quality of the scientific evidence raised by the FAIR community members.
GradInit: Learning to Initialize Neural Networks for Stable and  Efficient Training

Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision. Unfortunately, novel architectures often result in challenging hyper-parameter choices and training instability if the network parameters are not properly initialized. A number of architecture-specific initialization schemes have been proposed, but these schemes are not always portable to new architectures. This paper presents GradInit, an automated and architecture agnostic method for initializing neural networks. GradInit is based on a simple heuristic; the norm of each network layer is adjusted so that a single step of SGD or Adam with prescribed hyperparameters results in the smallest possible loss value. This adjustment is done by introducing a scalar multiplier variable in front of each parameter block, and then optimizing these variables using a simple numerical scheme. GradInit accelerates the convergence and test performance of many convolutional architectures, both with or without skip connections, and even without normalization layers. It also improves the stability of the original Transformer architecture for machine translation, enabling training it without learning rate warmup using either Adam or SGD under a wide range of learning rates and momentum coefficients. Code is available at https://github.com/zhuchen03/gradinit.
Toward a Bias-free Selection Criterion in Shear Measurement

Sample selection is a necessary preparation for weak lensing measurement. It is well-known that selection itself may introduce bias in the measured shear signal. Using image simulation and the Fourier_Quad shear measurement pipeline, we quantify the selection bias in various commonly used selection function (signal-to-noise-ratio, magnitude, etc.). We proposed a new selection function defined in the power spectrum of the galaxy image. This new selection function has low selection bias, and it is particularly convenient for shear measurement pipelines based on Fourier transformation.
Continuous time limit of the DTQW in 2D+1 and plasticity

A Plastic Quantum Walk admits both continuous time and continuous spacetime. The model has been recently proposed by one of the authors in \cite{molfetta2019quantum}, leading to a general quantum simulation scheme for simulating fermions in the relativistic and non relativistic regimes. The extension to two physical dimensions is still missing and here, as a novel result, we demonstrate necessary and sufficient conditions concerning which discrete time quantum walks can admit plasticity, showing the resulting Hamiltonians. We consider coin operators as general $4$ parameter unitary matrices, with parameters which are function of the lattice step size $\varepsilon$. This dependence on $\varepsilon$ encapsulates all functions of $\varepsilon$ for which a Taylor series expansion in $\varepsilon$ is well defined, making our results very general.
Are wider nets better given the same number of parameters?

Empirical studies demonstrate that the performance of neural networks improves with increasing number of parameters. In most of these studies, the number of parameters is increased by increasing the network width. This begs the question: Is the observed improvement due to the larger number of parameters, or is it due to the larger width itself? We compare different ways of increasing model width while keeping the number of parameters constant. We show that for models initialized with a random, static sparsity pattern in the weight tensors, network width is the determining factor for good performance, while the number of weights is secondary, as long as trainability is ensured. As a step towards understanding this effect, we analyze these models in the framework of Gaussian Process kernels. We find that the distance between the sparse finite-width model kernel and the infinite-width kernel at initialization is indicative of model performance.
Quantum state transmission through a spin chain in finite-temperature heat baths

Abstract Transmission of a quantum state is essential for performing quantum information processing tasks. The communication channel will be inevitably immersed in its surrounding environment under realistic conditions. In this paper, we investigate the influence of environment noise on the transmission fidelity when transferring a quantum state through a spin chain. The non-Markovian open system dynamics is systematically analyzed by using the quantum state diffusion equation method. With each spin immersed in its own finite temperature and non-Markovian heat bath, we consider three types of system–bath interaction: dephasing, dissipation and spin-boson. The transmission fidelity is found to decrease with the increasing bath temperature and system–bath coupling strength. Interestingly, we find that the bath non-Markovianity can help enhancing the transmission fidelity.
A Non-Invasive Context-Aware Dehydration Alert System

We propose Monitoring My Dehydration (MMD), a novel context-aware real-time Android-based tool that can instantly detect human dehydration level by utilizing wearable Electrodermal Activity (EDA) sensors in conjunction with signal processing and machine learning. Experimental evaluation on real-world collected data over 5 users provide improved dehydration estimation performance (84.5%) with satisfactory feasibility study and trustworthy explanation.
The Challenges of Machine Learning and Their Economic Implications

The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These issues can have substantial economic implications because they may hinder the development and mass adoption of machine learning. In light of this, the purpose of this paper was to determine, from a positive economics point of view, whether the free use of machine learning models maximizes aggregate social welfare or, alternatively, regulations are required. In cases in which restrictions should be enacted, policies are proposed. The adaptation of current tort and anti-discrimination laws is found to guarantee an optimal level of interpretability and fairness. Additionally, existing market solutions appear to incentivize machine learning operators to equip models with a degree of security and privacy that maximizes aggregate social welfare. These findings are expected to be valuable to inform the design of efficient public policies.
Studying Duplicate Logging Statements and Their Relationships With Code Clones

In this paper, we focus on studying duplicate logging statements, which are logging statements that have the same static text message. We manually studied over 4K duplicate logging statements and their surrounding code in five large-scale open source systems. We uncovered five patterns of duplicate logging code smells. For each instance of the duplicate logging code smell, we further manually identify the potentially problematic and justifiable cases. Then, we contact developers to verify our manual study result. We integrated our manual study result and the feedback of developers into our automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells. We evaluated DLFinder on the five manually studied systems and three additional systems. In total, combining the results of DLFinder and our manual analysis, we reported 91 problematic duplicate logging code smell instances to developers and all of them have been fixed. We further study the relationship between duplicate logging statements, including the problematic instances of duplicate logging code smells, and code clones. We find that 83% of the duplicate logging code smell instances reside in cloned code, but 17% of them reside in micro-clones that are difficult to detect using automated clone detection tools. We also find that more than half of the duplicate logging statements reside in cloned code snippets, and a large portion of them reside in very short code blocks which may not be effectively detected by existing code clone detection tools. Our study shows that, in addition to general source code that implements the business logic, code clones may also result in bad logging practices that could increase maintenance difficulties.
Application of multivariate regression on magnetic data to determine further drilling site for iron exploration

Abstract In this study, a new approach of the multivariate regression model has been applied to make a precise mathematical model to determine further drilling for the detailed iron exploration in the Koohbaba area, Northwest of Iran. Furthermore, to figure out the additional drilling locations, the ore length to the total core ratio for the drilled boreholes has been used based on the geophysical exploration dataset. Hence, different regression analyses including linear, cubic, and quadratic models have been applied. In this study, the ore length to the total core ratio of the chosen drilled boreholes has been considered as a dependent variable; besides, the outputs of the magnetic data using the UP10 (10m upward-continuation), RTP (reduction to the pole), and A.S. (analytic signal) techniques have been designated as independent variables. Based on probability value ( p -value), coefficients of determination ( R 2 and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msubsup> <m:mrow> <m:mi>R</m:mi> </m:mrow> <m:mrow> <m:mtext>adj</m:mtext> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msubsup> </m:math> {R}_{\text{adj}}^{2} ), and efficiency formula (EF), the fourth regression model has revealed the best results. The accuracy of the model has been confirmed by the defined ratio of boreholes and demonstrated by four additional drilled boreholes in the study area. Therefore, the results of the regression analysis are reasonable and can be used to determine the additional drilling for the detailed exploration.
Translation of the Lampung Language Text Dialect of Nyo into the Indonesian Language with DMT and SMT Approach

Research on the translation of Lampung language text dialect of Nyo into Indonesian is done with two approaches, namely Direct Machine Translation (DMT) and Statistical Machine Translation (SMT). This research experiment was conducted as a preliminary effort in helping students immigrants in the province of Lampung, translating the Lampung language dialect of Nyo through prototypes or models was built. In the DMT approach, the dictionary is used as the primary tool. In contrast, in SMT, the parallel corpus of Lampung Nyo and Indonesian language is used to make language models and translation models using Moses Decoder. The result of text translation accuracy with the DMT approach is 39.32%, and for the SMT approach is 59.85%. Both approaches use Bilingual Evaluation Understudy (BLEU) assessment.
On Distributed Differential Privacy and Counting Distinct Elements

We study the setup where each of $n$ users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of $(\epsilon, \delta)$-differentially privacy: - In the non-interactive local setting, we prove that the additive error of any protocol is $\Omega(n)$ for any constant $\epsilon$ and for any $\delta$ inverse polynomial in $n$. - In the single-message shuffle setting, we prove a lower bound of $\Omega(n)$ on the error for any constant $\epsilon$ and for some $\delta$ inverse quasi-polynomial in $n$. We do so by building on the moment-matching method from the literature on distribution estimation. - In the multi-message shuffle setting, we give a protocol with at most one message per user in expectation and with an error of $\tilde{O}(\sqrt(n))$ for any constant $\epsilon$ and for any $\delta$ inverse polynomial in $n$. Our protocol is also robustly shuffle private, and our error of $\sqrt(n)$ matches a known lower bound for such protocols. Our proof technique relies on a new notion, that we call dominated protocols, and which can also be used to obtain the first non-trivial lower bounds against multi-message shuffle protocols for the well-studied problems of selection and learning parity. Our first lower bound for estimating the number of distinct elements provides the first $\omega(\sqrt(n))$ separation between global sensitivity and error in local differential privacy, thus answering an open question of Vadhan (2017). We also provide a simple construction that gives $\tilde{\Omega}(n)$ separation between global sensitivity and error in two-party differential privacy, thereby answering an open question of McGregor et al. (2011).