Reviewerly/exHarmony huggingface dataset:
Value conflicts and information security – a mixed-methods study in high-risk industry

Purpose The purpose of this study is to investigate the influence of work-related value conflicts on information security in two organisations in nuclear power production and related industry. Design/methodology/approach A mixed-methods design was applied. Individual interviews were conducted with 24 employees of two organisations in Sweden and questionnaire data on information security climate were collected from 667 employees (62%) in the same two organisations. Findings The qualitative part of the study identified five different types of value conflicts influencing information security behaviour. The quantitative part of the study found that value conflicts relating to information security had a negative relationship with rule-compliant behaviour. The opposite was found for participative security behaviour where there was a positive relationship with value conflicts. A high climate of information security was positively related to both rule-compliant and participative information security behaviour. It also moderated the effect of value conflicts on compliant information security behaviour. Originality/value This paper highlights organisational contextual conditions that influence employees’ motivation and ability to manage value conflicts relating to information security in a high-risk industry. It also enables a better understanding of the influence of the information security climate on information security in the presence of value conflicts in this type of industry.
Design and Development of an Algorithm to Secure Big Data on Cloud Computing

In any organization, data is coming from various online sources and various offline sources. It is very difficult to equipment huge amount of data. Big data is one of the technologies who tackle huge amount of data easily. Cloud computing is a platform for big data. Although there are vast number of advantages for big data and cloud computing, they failed to make their place in people heart as people are anxious about security of their data. They are huge number of advantages we gain if we move big data on cloud computing like on-demand service availability, availability of data and information on Internet, resources grouping, easy to manage, easy to analyze large volume of big data, and most important is cost effective. Although there are numerous advantages to moving big data on cloud computing, we cannot avoid challenges to big data and cloud computing. The various challenges to big data are cost, data quality, rapid change, skilled man power requirement, infrastructure need, moving data onto big data platform, need for synchronization across data sources, and most important is security of data. These all challenges are not going to be easily solved; various researches are going on to solve all these problems. In this paper, we provide review about various research done in the area of security of big data on cloud computing, and finally, we provide research plan for our project regarding security of big data on cloud computing.
Inverting Gradient Attack Combined with GAN Network in Federated Learning of Face Recognition

In recent years, federated learning is often used as a means to protect data privacy and is widely used in face recognition and other privacy-related scenarios. In the past, it was generally believed that the gradient information uploaded to the server would not leak the original training data. However, it has been discovered that an efficient gradient leakage attack can restore the original data only through the gradient information. In the scene of face recognition, since the facial features contain complex information, the attack effect of this traditional gradient leakage attack is not ideal. This paper aims at the face recognition task in the federated learning scenario, for the first time through the constraints and round optimization of Face GAN, to indicate the best gradient descent direction for the training of the attack network model, so as to avoid falling into the local minimum, to improve the traditional gradient leakage attack. Experiments show that when the batch size=1 of the proposed scheme, after adding noise to the picture, our optimization scheme can significantly improve the anti-interference ability of the attack. The facial features recovered are clearer, the face is more natural, and the error with the original data is smaller, the error is reduced by 80% at most; when batch size>1, the improved attack scheme can prevent multiple pictures from being mixed on one picture, thereby recovering a larger number of pictures.
Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512

The authors wish to make the following correction to their paper [...].
Interaction patterns within the educational framework: the teacher - the learner

This research aims to analyse the term interaction, through which experiences are exchanged in their knowledge and skill aspects, between the parties to the interaction. On the other hand, language has multiple formats, and interactive formats. Which carries common cultures and social customs. Therefore, linguistic interaction is the mainstay in linking social relations. The interaction within the classroom is one of the most important factors that increase the effectiveness of the educational process. There are three types of interaction that can occur in the educational process, interaction between the teacher and students, interaction between the teacher and one student, and interaction between the students themselves in the class. Keywords: interaction, learner, teacher, continuous, learning, communication, reception.
Contracts Used to Procure Cloud Services II

The chapter examines how general contract law, as defined in the chapter, will likely apply to the use of cloud computing services. This chapter focuses on terms that are often considered standard in cloud agreements. The analysis includes terms aimed at keeping information confidential, non-disclosure agreements, terms regarding liability, warranties, and other terms and conditions aimed at regulating or limiting responsibility. Additionally, the chapter considers terms aimed at termination of services, portability and other provisions necessary for exiting services.
Research on the data extraction method of lip shape feature based on facial motion capture technology

This paper uses facial motion capture technology to extract the dynamic trajectory of the lip shape change of a person during pronunciation. According to the MPEG-4 standard, the face marking points of the speaker are defined. While analyzing the method of extracting parameters of upper lip protrusion and outer lip width, it focuses on the extraction method of lower lip protrusion and the process of data processing. The data obtained by this method is more accurate, which has a high reference value for analyzing lip features in the research of pronunciation phonetics. This method can also be used for research in various languages.
How Words Are Studied

This chapter tackles questions such as how to differentiate a word from a nonword and what features should be ascribed to a word.
A 45.4x∼221.2x latency Improvement of SRP-5 Cryptographic Engine for Smart Grid Network

In a smart grid network, smart meters transmit user’s real-time data to the power management center, while the users receive time-based pricing information from the power management center. As shown in Fig. 1, a secure two-way communication is needed to be implemented for mutual authentication with key management between the power management center and smart meters. Since the prior work [1] adopting SRP-3 as the authentication steps of the smart grid network will be limited in RSA execution for resource constraint devices, a new SRP-5 [2] cryptographic engine featured in supporting Elliptic Curve Cryptography (ECC), and hardware/software co-design is presented in this paper.
Quantum algorithms for the generalized eigenvalue problem

The generalized eigenvalue (GE) problems are of particular importance in various areas of science engineering and machine learning. We present a variational quantum algorithm for finding the desired generalized eigenvalue of the GE problem, $\mathcal{A}|\psi\rangle=\lambda\mathcal{B}|\psi\rangle$, by choosing suitable loss functions. Our approach imposes the superposition of the trial state and the obtained eigenvectors with respect to the weighting matrix $\mathcal{B}$ on the Rayleigh-quotient. Furthermore, both the values and derivatives of the loss functions can be calculated on near-term quantum devices with shallow quantum circuit. Finally, we propose a full quantum generalized eigensolver (FQGE) to calculate the minimal generalized eigenvalue with quantum gradient descent algorithm. As a demonstration of the principle, we numerically implement our algorithms to conduct a 2-qubit simulation and successfully find the generalized eigenvalues of the matrix pencil $(\mathcal{A},\,\mathcal{B})$. The numerically experimental result indicates that FQGE is robust under Gaussian noise.
Analysis of matter clustering in one dimension with the Gaussian-derived  wavelet

Continuous wavelet analysis has been increasingly employed in various fields of science and engineering due to its remarkable ability to maintain optimal resolution in both space and scale. Here, we extend wavelet-based statistics, including the wavelet power spectrum, wavelet cross-correlation and wavelet bicoherence, to the new designed continuous wavelet function -- {\em Gaussian-derived wavelet}. In this paper, these statistics are introduced to analyze the large-scale clustering of matter. For this purpose, we perform wavelet transforms on the density distribution obtained from the one-dimensional (1D) Zel'dovich approximation and then measure the wavelet power spectra and wavelet bicoherences of this density distribution. Our results suggest that the wavelet power spectrum and wavelet bicoherence can identify the effects of local environments on the clustering at different scales. Moreover, to reveal the usefulness of the wavelet cross-correlation, we apply it to the 1D projected density fields of the IllustrisTNG simulation at $z=0$ for different matter components. We find that wavelet cross-correlations between different matter components converge to one on large scales, while biases between them become significant on small scales. In addition, measurements of the wavelet power spectra show that clustering of the total matter is suppressed on scales $k\gtrsim 1 h\mathrm{Mpc}^{-1}$ relative to that of the corresponding dark matter-only simulation. The wavelet bicoherence of the total matter is enhanced on wide scales due to baryonic physics. These results are qualitatively consistent with those from three-dimensional Fourier analyses.
Activation to Saliency: Forming High-Quality Labels for Unsupervised  Salient Object Detection

Unsupervised Salient Object Detection (USOD) is of paramount significance for both industrial applications and downstream tasks. Existing deep-learning (DL) based USOD methods utilize some low-quality saliency predictions extracted by several traditional SOD methods as saliency cues, which mainly capture some conspicuous regions in images. Furthermore, they refine these saliency cues with the assistant of semantic information, which is obtained from some models trained by supervised learning in other related vision tasks. In this work, we propose a two-stage Activation-to-Saliency (A2S) framework that effectively generates high-quality saliency cues and uses these cues to train a robust saliency detector. More importantly, no human annotations are involved in our framework during the whole training process. In the first stage, we transform a pretrained network (MoCo v2) to aggregate multi-level features to a single activation map, where an Adaptive Decision Boundary (ADB) is proposed to assist the training of the transformed network. To facilitate the generation of high-quality pseudo labels, we propose a loss function to enlarges the feature distances between pixels and their means. In the second stage, an Online Label Rectifying (OLR) strategy updates the pseudo labels during the training process to reduce the negative impact of distractors. In addition, we construct a lightweight saliency detector using two Residual Attention Modules (RAMs), which refine the high-level features using the complementary information in low-level features, such as edges and colors. Extensive experiments on several SOD benchmarks prove that our framework reports significant performance compared with existing USOD methods. Moreover, training our framework on 3000 images consumes about 1 hour, which is over 30x faster than previous state-of-the-art methods.
Improving Spectral Graph Convolution for Learning Graph-level  Representation

From the original theoretically well-defined spectral graph convolution to the subsequent spatial bassed message-passing model, spatial locality (in vertex domain) acts as a fundamental principle of most graph neural networks (GNNs). In the spectral graph convolution, the filter is approximated by polynomials, where a $k$-order polynomial covers $k$-hop neighbors. In the message-passing, various definitions of neighbors used in aggregations are actually an extensive exploration of the spatial locality information. For learning node representations, the topological distance seems necessary since it characterizes the basic relations between nodes. However, for learning representations of the entire graphs, is it still necessary to hold? In this work, we show that such a principle is not necessary, it hinders most existing GNNs from efficiently encoding graph structures. By removing it, as well as the limitation of polynomial filters, the resulting new architecture significantly boosts performance on learning graph representations. We also study the effects of graph spectrum on signals and interpret various existing improvements as different spectrum smoothing techniques. It serves as a spatial understanding that quantitatively measures the effects of the spectrum to input signals in comparison to the well-known spectral understanding as high/low-pass filters. More importantly, it sheds the light on developing powerful graph representation models.
Trainability for Universal GNNs Through Surgical Randomness

Message passing neural networks (MPNN) have provable limitations, which can be overcome by universal networks. However, universal networks are typically impractical. The only exception is random node initialization (RNI), a data augmentation method that results in provably universal networks. Unfortunately, RNI suffers from severe drawbacks such as slow convergence and high sensitivity to changes in hyperparameters. We transfer powerful techniques from the practical world of graph isomorphism testing to MPNNs, resolving these drawbacks. This culminates in individualization-refinement node initialization (IRNI). We replace the indiscriminate and haphazard randomness used in RNI by a surgical incision of only a few random bits at well-selected nodes. Our novel non-intrusive data-augmentation scheme maintains the networks' universality while resolving the trainability issues. We formally prove the claimed universality and corroborate experimentally -- on synthetic benchmarks sets previously explicitly designed for that purpose -- that IRNI overcomes the limitations of MPNNs. We also verify the practical efficacy of our approach on the standard benchmark data sets PROTEINS and NCI1.
CaSP: Class-agnostic Semi-Supervised Pretraining for Detection and  Segmentation

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either very task-unrelated or very task-specific training signals from unlabeled data. We argue that these two approaches, at the two extreme ends of the task-specificity spectrum, are suboptimal for the task performance. Utilizing too little task-specific training signals causes underfitting to the ground-truth labels of downstream tasks, while the opposite causes overfitting to the ground-truth labels. To this end, we propose a novel Class-agnostic Semi-supervised Pretraining (CaSP) framework to achieve a more favorable task-specificity balance in extracting training signals from unlabeled data. Compared to semi-supervised learning, CaSP reduces the task specificity in training signals by ignoring class information in the pseudo labels and having a separate pretraining stage that uses only task-unrelated unlabeled data. On the other hand, CaSP preserves the right amount of task specificity by leveraging box/mask-level pseudo labels. As a result, our pretrained model can better avoid underfitting/overfitting to ground-truth labels when finetuned on the downstream task. Using 3.6M unlabeled data, we achieve a remarkable performance gain of 4.7% over ImageNet-pretrained baseline on object detection. Our pretrained model also demonstrates excellent transferability to other detection and segmentation tasks/frameworks.
Adaptive Projected Residual Networks for Learning Parametric Maps from  Sparse Data

We present a parsimonious surrogate framework for learning high dimensional parametric maps from limited training data. The need for parametric surrogates arises in many applications that require repeated queries of complex computational models. These applications include such "outer-loop" problems as Bayesian inverse problems, optimal experimental design, and optimal design and control under uncertainty, as well as real time inference and control problems. Many high dimensional parametric mappings admit low dimensional structure, which can be exploited by mapping-informed reduced bases of the inputs and outputs. Exploiting this property, we develop a framework for learning low dimensional approximations of such maps by adaptively constructing ResNet approximations between reduced bases of their inputs and output. Motivated by recent approximation theory for ResNets as discretizations of control flows, we prove a universal approximation property of our proposed adaptive projected ResNet framework, which motivates a related iterative algorithm for the ResNet construction. This strategy represents a confluence of the approximation theory and the algorithm since both make use of sequentially minimizing flows. In numerical examples we show that these parsimonious, mapping-informed architectures are able to achieve remarkably high accuracy given few training data, making them a desirable surrogate strategy to be implemented for minimal computational investment in training data generation.
Representation Learning for Conversational Data using Discourse Mutual  Information Maximization

Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding. Prior works usually relied on finetuned representations based on generic text representation models like BERT or GPT-2. But, existing pretraining objectives do not take the structural information of text into consideration. Although generative dialog models can learn structural features too, we argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling. We empirically demonstrate that such representations do not perform consistently across various dialog understanding tasks. Hence, we propose a structure-aware Mutual Information based loss-function DMI (Discourse Mutual Information) for training dialog-representation models, that additionally captures the inherent uncertainty in response prediction. Extensive evaluation on nine diverse dialog modeling tasks shows that our proposed DMI-based models outperform strong baselines by significant margins, even with small-scale pretraining. Our models show the most promising performance on the dialog evaluation task DailyDialog++, in both random and adversarial negative scenarios.
Pool-Party: Exploiting Browser Resource Pools as Side-Channels for Web  Tracking

We identify a new class of side-channels in browsers that are not mitigated by current defenses. This class of side-channels, which we call "pool-party" attacks, allow sites to create covert channels by manipulating limited-but-unpartitioned resource pools in browsers. We identify pool-party attacks in all popular browsers, and show they are practical cross-site tracking techniques. In this paper we make the following contributions: first, we describe pool-party side-channel attacks that exploit limits in application-layer resource pools in browsers. Second, we demonstrate that pool-party attacks are practical, and can be used to track users in all popular browsers; we also share open source implementations of the attack and evaluate them through a representative web crawl. Third, we show that in Gecko based-browsers (including the Tor Browser Bundle) pool-party attacks can also be used for cross-profile tracking (e.g., linking user behavior across normal and private browsing sessions). Last, we discuss possible mitigations and defenses.
DiffuseMorph: Unsupervised Deformable Image Registration Along  Continuous Trajectory Using Diffusion Models

Deformable image registration is one of the fundamental tasks for medical imaging and computer vision. Classical registration algorithms usually rely on iterative optimization approaches to provide accurate deformation, which requires high computational cost. Although many deep-learning-based methods have been developed to carry out fast image registration, it is still challenging to estimate the deformation field with less topological folding problem. Furthermore, these approaches only enable registration to a single fixed image, and it is not possible to obtain continuously varying registration results between the moving and fixed images. To address this, here we present a novel approach of diffusion model-based probabilistic image registration, called DiffuseMorph. Specifically, our model learns the score function of the deformation between moving and fixed images. Similar to the existing diffusion models, DiffuseMorph not only provides synthetic deformed images through a reverse diffusion process, but also enables various levels of deformation of the moving image along with the latent space. Experimental results on 2D face expression image and 3D brain image registration tasks demonstrate that our method can provide flexible and accurate deformation with a capability of topology preservation.
Hurwitz numbers for reflection groups I: Generatingfunctionology

The classical Hurwitz numbers count the fixed-length transitive transposition factorizations of a permutation, with a remarkable product formula for the case of minimum length (genus $0$). We study the analogue of these numbers for reflection groups with the following generalization of transitivity: say that a reflection factorization of an element in a reflection group $W$ is full if the factors generate the whole group $W$. We compute the generating function for full factorizations of arbitrary length for an arbitrary element in a group in the combinatorial family $G(m, p, n)$ of complex reflection groups in terms of the generating functions of the symmetric group $\mathfrak{S}_n$ and the cyclic group of order $m/p$. As a corollary, we obtain leading-term formulas which count minimum-length full reflection factorizations of an arbitrary element in $G(m,p,n)$ in terms of the Hurwitz numbers of genus $0$ and $1$ and number-theoretic functions. We also study the structural properties of such generating functions for any complex reflection group; in particular, we show via representation-theoretic methods that they can by expressed as finite sums of exponentials of the variable.