Reviewerly/exHarmony huggingface dataset:
Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior

Non-negative matrix factorization (NMF) is generally an ill-posed problem which requires further regularization. Regularization of NMF using the assumption of sparsity is common as well as regularization using smoothness. In many applications it is natural to assume that both of these assumptions hold together. To avoid <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> combination of these assumptions using weighting coefficient, we formulate the problem using a probabilistic model and estimate it in a Bayesian way. Specifically, we use the fact that the assumptions of sparsity and smoothness are different forms of prior covariance matrix modeling. We use a generalized model that includes both sparsity and smoothness as special cases and estimate all its parameters using the variational Bayes method. The resulting matrix factorization algorithm is compared with state-of-the-art algorithms on large clinical dataset of 196 image sequences from dynamic renal scintigraphy. The proposed algorithm outperforms other algorithms in statistical evaluation.
Differential privacy for learning vector quantization

Prototype-based machine learning methods such as learning vector quantisation (LVQ) offer flexible classification tools, which represent a classification in terms of typical prototypes. This representation leads to a particularly intuitive classification scheme, since prototypes can be inspected by a human partner in the same way as data points. Yet, it bears the risk of revealing private information included in the training data, since individual information of a single training data point can significantly influence the location of a prototype. In this contribution, we investigate the question how to algorithmically extend LVQ such that it provably obeys privacy constraints as offered by the notion of so-called differential privacy. More precisely, we demonstrate the sensitivity of LVQ to single data points and hence the need of its extension to private variants in case of possibly sensitive training data. We investigate three technologies which have been proposed in the context of differential privacy, and we extend these technologies to LVQ schemes. We investigate the effectiveness and efficiency of these schemes for various data sets, and we evaluate their scalability and robustness as regards the choice of meta-parameters and characteristics of training sets. Interestingly, one algorithm, which has been proposed in the literature due to its beneficial mathematical properties, does not scale well with data dimensionality, while two alternative techniques, which are based on simpler principles, display good results in practical settings.
Eudaimonia and Hedonia in the Design and Evaluation of a Cooperative Game for Psychosocial Well-Being

An extended design and evaluation framework of eudaimonia (personal growth, expressiveness) and hedonia (pleasure, comfort) was applied to a cooperative game for older adults who rely on power mobility. The purpose was to address two psychosocial well-being needs (perceptions of performance mastery and empathy enhancement) through a game with an interaction format that augments the experience of powered chair use: mixed reality with power mobility-based interaction and movement. Two versions of the game—one in a mixed reality format and one without—were created to evaluate the efficacy of using this framework and explore relevant user experience factors. Qualitative findings suggest that the mixed reality version of the game elicited eudaimonic experiences, where the older adult participants who were typically reliant on power mobility perceived a positive change in performance mastery while their partners, who were not reliant on power mobility developed a greater appreciation for everyday powered chair use. Further, a role reversal, where the elders took on a ‘caregiving’ role in the context of powered chair expertise, was observed. Moreover, inferential findings showed evidence of a predictive relationship between hedonic and eudaimonic orientations, psychological well-being, and game engagement, regardless of age, disability status, and game version.
Toward Hardware-Rooted Smartphone Authentication

In recent years, an enormous amount of research has been carried out in an attempt to identify smartphones through modeling the manufacturing imperfections of their built-in transducers, which spawned a rich set of hardware fingerprinting methods. However, the vast majority of the work in this area has focused on device tracking and identification. For adversarial settings like forensics and authentication, it remains unclear whether these methods will provide reliable identification results when the outputs of transducers are tampered by adversaries intentionally. In this article, we look into the authentication scenario and investigate the feasibility of using a hardware fingerprint as the unique identity of a smartphone. We discuss various security issues underlying hardware-rooted smartphone authentication and outline two specific attacks that need to be taken into account while designing such a system. We also describe several desirable properties that a fingerprinting method should have in order to be applicable for the authentication scenario. Several classic fingerprinting methods are studied, and their security and usability are analyzed.
Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors

Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks.
General Plane-Based Clustering With Distribution Loss

In this article, we propose a general model for plane-based clustering. The general model reveals the relationship between cluster assignment and cluster updating during clustering implementation, and it contains many existing plane-based clustering methods, e.g., k-plane clustering, proximal plane clustering, twin support vector clustering, and their extensions. Under this general model, one may obtain an appropriate clustering method for a specific purpose. The general model is a procedure corresponding to an optimization problem, which minimizes the total loss of the samples. Thereinto, the loss of a sample derives from both within-cluster and between-cluster information. We discuss the theoretical termination conditions and prove that the general model terminates in a finite number of steps at a local or weak local solution. Furthermore, we propose a distribution loss function that fluctuates with the input data and introduce it into the general model to obtain a plane-based clustering method (DPC). DPC can capture the data distribution precisely because of its statistical characteristics, and its termination that finitely terminates at a weak local solution is given immediately based on the general model. The experimental results show that our DPC outperforms the state-of-the-art plane-based clustering methods on many synthetic and benchmark data sets.
Secret key rate proof of multicarrier continuous‐variable quantum key distribution

Summary We prove the secret key rate formulas and derive security threshold parameters of multicarrier continuous‐variable quantum key distribution CVQKD. In a multicarrier CVQKD scenario, the Gaussian input quantum states of the legal parties are granulated into Gaussian subcarrier continuous variables (CVs). The multicarrier communication formulates Gaussian subchannels from the physical quantum channel, each dedicated to the transmission of a subcarrier CV. The Gaussian subcarriers are decoded by a unitary CV operation, which results in the recovered single‐carrier Gaussian CVs. We derive the formulas through the adaptive multicarrier quadrature division (AMQD) scheme, the singular value decomposition (SVD)–assisted AMQD, and the multiuser AMQD multiuser quadrature allocation (MQA). We prove that the multicarrier CVQKD leads to improved secret key rates and higher tolerable excess noise in comparison with single‐carrier CVQKD. We derive the private classical capacity of a Gaussian subchannel and the security parameters of an optimal Gaussian collective attack in the multicarrier setting. We reveal the secret key rate formulas for one‐way and two‐way multicarrier CVQKD protocols, assuming homodyne and heterodyne measurements and direct and reverse reconciliation. The results reveal the physical boundaries of physically allowed Gaussian attacks in a multicarrier CVQKD scenario and confirm that the improved transmission rates lead to enhanced secret key rates and security thresholds.
Academic Library Collections in the Age of Extended Reality (XR)

Extended Reality (XR) introduces a new way to preserve, record, and manage content. Collections traditionally focus on content in the form of books, documents, and multimedia. XR is a new form of media that can be difficult to integrate into current collections. In addition, through linked data, we can preserve the context that surrounds the content as well. Finally, XR media can incorporate digital manifestations of items from a library collection within its application. This technology review will explore the possibilities of XR in collection management, focusing on XR as a user interface, the impact on inventory management, and digital preservation.
RiSER: Learning Better Representations for Richly Structured Emails

Recent studies show that an overwhelming majority of emails are machine-generated and sent by businesses to consumers. Many large email services are interested in extracting structured data from such emails to enable intelligent assistants. This allows experiences like being able to answer questions such as “What is the address of my hotel in New York?” or “When does my flight leave?”. A high-quality email classifier is a critical piece in such a system. In this paper, we argue that the rich formatting used in business-to-consumer emails contains valuable information that can be used to learn better representations. Most existing methods focus only on textual content and ignore the rich HTML structure of emails. We introduce RiSER (Richly Structured Email Representation) - an approach for incorporating both the structure and content of emails. RiSER projects the email into a vector representation by jointly encoding the HTML structure and the words in the email. We then use this representation to train a classifier. To our knowledge, this is the first description of a neural technique for combining formatting information along with the content to learn improved representations for richly formatted emails. Experimenting with a large corpus of emails received by users of Gmail, we show that RiSER outperforms strong attention-based LSTM baselines. We expect that these benefits will extend to other corpora with richly formatted documents. We also demonstrate with examples where leveraging HTML structure leads to better predictions.
Tight Reductions for Diffie-Hellman Variants in the Algebraic Group Model

Fuchsbauer, Kiltz, and Loss (Crypto'18) gave a simple and clean definition of an algebraic group model (AGM) that lies in between the standard model and the generic group model (GGM). Specifically, an algebraic adversary is able to exploit group-specific structures as the standard model while the AGM successfully provides meaningful hardness results as the GGM. As an application of the AGM, they show a tight computational equivalence between the computing Diffie-Hellman (CDH) assumption and the discrete logarithm (DL) assumption. For the purpose, they used the square Diffie-Hellman assumption as a bridge, i.e., they first proved the equivalence between the DL assumption and the square Diffie-Hellman assumption, then used the known equivalence between the square Diffie-Hellman assumption and the CDH assumption. In this paper, we provide an alternative proof that directly shows the tight equivalence between the DL assumption and the CDH assumption. The crucial benefit of the direct reduction is that we can easily extend the approach to variants of the CDH assumption, e.g., the bilinear Diffie-Hellman assumption. Indeed, we show several tight computational equivalences and discuss applicabilities of our techniques.
Improving the SVM gender classification accuracy using clustering and incremental learning

Abstract Gender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.
MRI denoising by NeighShrink based on chi-square unbiased risk estimation

NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Therefore, we propose the NeighShrinkCURE denoising algorithm. Bilateral filtering and cycle spinning are used to further improve denoising performance. Experimental results show that the proposed algorithm is simple and efficient, and provides good noise reduction while preserving edges and details well. Compared with some similar MRI denoising algorithms, the proposed algorithm has improvements in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Although running time of the proposed algorithm has some increment compared with some current MRI denoising algorithms, the comprehensive performance of the proposed algorithm is superior to several current MRI denoising algorithms.
An Approach to Estimating of Criticality of Social Engineering Attacks Traces

In this article we propose to consider the trajectories of social engineering attacks, which are the most critical from the point of view of the expected damage to the organization, and not from the point of view of the probability of success of the defeat of the user and, indirectly, critical documents to which he has access. The article proposes an approach to solving the problem of identifying the most critical path of multiway socio-engineering attack. The most critical trajectory in this article is understood as the most probable trajectory of the attack, which will bring the greatest damage to the organization. As a further development of the research direction, we can consider models that describe in more detail the context and take into account the distribution of the probability of hitting the proportion of documents available to the user, offering models for building integrated damage estimates associated with the affected user, various access policies and accounting for the hierarchy of documents in terms of their criticality or value.
RETRACTED: Flow online identification method for the encrypted Skype

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of at the request of the Editor-in-Chief and stated co-author Raj Jain. The corresponding author, Shi Dong, has submitted this article without the consent and knowledge of the stated co-author, using a fake email address. Using someone else’s name as an author represents a clear violation of the fundamentals of peer review, our publishing policies, and publishing ethics standards. Apologies are offered to readers of the article.
Network Flow Labeling for Extended Target Tracking PHD Filters

The probability hypothesis density (PHD) filter is a method for tracking multiple target objects based on unlabeled detections. However, as the PHD filter employs a first-order approximation of random finite sets, it does not provide track labels, i.e., targets of consecutive time steps are not associated with each other. In this paper, an intuitive and efficient labeling strategy on top of the extended target PHD filter is proposed. The approach is based on solving a network flow problem and makes use of the Wasserstein metric to account for the spatial extent of the objects. The resulting tracker is evaluated with laser scanner data from two traffic scenarios.
Fracture Segmentation Method Based on Contour Evolution and Gradient Direction Consistency in Sequence of Coal Rock CT Images

The coal rock exhibits obvious heterogeneity and anisotropy after a long-term geological evolution in nature. Therefore, the coal rock CT (computed tomography) image shows uneven grey scale, low contrast, and the fractures with weak boundaries. The accurate segmentation of the coal rock fracture networks is challenging. In this paper, a segmentation method of fractures based on contour evolution and gradient direction consistency is proposed to accurately segment the fracture networks in the sequence of coal rock CT images. According to the contour variation rule of the fractures in the discrete 3D (three dimensional) space formed by the sequence of CT images, the fracture contour evolution model (FCEM) is constructed and the preliminary segmentation results of fractures are obtained from FCEM. A 3D adaptive median filtering (3DAMF) and a 3D bilateral filtering (3DBF) are proposed. The high density miscellaneous point noises in the coal rock CT images are filtered by the 3DAMF. And the boundaries of fractures are enhanced by 3DBF. According to the similarity of the preliminary segmentation results of fractures and the real contours of fractures, the preliminary segmentation results of fractures are optimized based on the gradient direction consistency model (GDCM) proposed in this paper to obtain the accurate boundaries of fractures. The fracture segmentation method proposed in this paper can obtain accurate boundaries of fractures with weak boundaries, and the experimental results show that the segmentation efficiency for sequence is high and adaptability is strong.
Gaussian kernels for incomplete data

This paper discusses a method to estimate the expected value of the Gaussian kernel in the presence of incomplete data. We show how, under the general assumption of a missing-at-random mechanism, the expected value of the Gaussian kernel function has a simple closed-form solution. Such a solution depends only on the parameters of the Gamma distribution which is assumed to represent squared distances. Furthermore, we show how the parameters governing the Gamma distribution depend only on the non-central moments of the kernel arguments, via the second-order moments of their squared distance, and can be estimated by making use of any parametric density estimation model of the data distribution. We approximate the data distribution with the maximum likelihood estimate of a Gaussian mixture distribution. The validity of the method is empirically assessed, under a range of conditions, on synthetic and real problems and the results compared to existing methods. For comparison, we consider methods that indirectly estimate a Gaussian kernel function by either estimating squared distances or by imputing missing values and then computing distances. Based on the experimental results, the proposed method consistently proves itself an accurate technique that further extends the use of Gaussian kernels with incomplete data.
QuantumIS: A Qualia Consciousness Awareness and Information Theory Quale Approach to Reducing Strategic Decision-Making Entropy

This paper investigates the underlying driving force in strategic decision-making. From a conceptual standpoint, few studies empirically studied the decision-maker's intrinsic state composed of entropy and uncertainty. This study examines a mutual information theory approach integrated into a state of qualia complexity that minimizes exclusion and maximizes the interactions of the information system and its dynamic environment via logical metonymy, illusion, and epigenetics. The article questions whether decision-makers at all levels of the organization are responding from the consciousness of an objective quale from a more subjective qualia awareness in the narrow-sense perspective of individual instances of their conscious experience. To quantify this research question, we explore several hypotheses revolving around strategic information system decisions. In this research, we posit that the eigenvalues of factor analysis along with the reduction in the uncertainty coefficients of the qualia entropy will be balanced by the quale enthalpy of our information theory structural equation model of trust, flexibility, expertise, top management support, and competitive advantage performance. We operationalize the integration of the aforementioned top management support, information systems competencies, and competitive advantage performance concepts into the qualia consciousness awareness and information theory quale framework.