Reviewerly/exHarmony huggingface dataset:
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups

Data sets sampled in Lie groups are widespread, and as with multivariate data, it is important for many applications to assess the differences between the sets in terms of their distributions. Indices for this task are usually derived by considering the Lie group as a Riemannian manifold. Then, however, compatibility with the group operation is guaranteed only if a bi-invariant metric exists, which is not the case for most non-compact and non-commutative groups. We show here that if one considers an affine connection structure instead, one obtains bi-invariant generalizations of well-known dissimilarity measures: a Hotelling $T^2$ statistic, Bhattacharyya distance and Hellinger distance. Each of the dissimilarity measures matches its multivariate counterpart for Euclidean data and is translation-invariant, so that biases, e.g., through an arbitrary choice of reference, are avoided. We further derive non-parametric two-sample tests that are bi-invariant and consistent. We demonstrate the potential of these dissimilarity measures by performing group tests on data of knee configurations and epidemiological shape data. Significant differences are revealed in both cases.
A System For Storing Anonymous Patient Healthcare Data Using Blockchain And Its Applications

Abstract In this paper, a system is proposed which uses blockchain technology in healthcare. In this system, patients can access their health records anytime from anywhere. Moreover, the patients’ health records are put into the blockchain anonymously. Whenever a patient visits a healthcare professional, the authorized entity filters patients’ medical report out by eliminating the patients’ sensitive information. Then, the filtered medical data are put into an off-chain database, while the address of the data is put into the blockchain with an assigned pseudo random identity of the patient. Thus, there are multi pseudo random identities for each patient. Unlike previous studies where the patients’ identities/reports were linkable, in the proposed protocol the patients’ identities are not linkable. The proposed system can also be used to show patients’ health status to some entities when a pandemic happens (e.g. COVID-19). During the COVID-19 pandemic, the patients are required to show their series of vaccinations before they travel internationally/nationally or participate in some social events. To travel or join some events, the patient needs to show only a partial medical history to the security guard without leaking any private information. Furthermore, once the anonymous medical data are put into the off-chain database, the data can be used for data mining and machine learning.
Distributed Partial Simulation for Graph Pattern Matching

Abstract Pattern matching in big graphs is important for different modern applications. Recently, this problem was defined in terms of multiple extensions of graph simulation, to reduce complexity and capture more meaningful results. These results were achieved through the relaxation of commonly used constraint in subgraph isomorphism pattern matching. Nevertheless, these graph simulation variant models are still too strict to provide results in many cases, especially when analyzed graphs contain anomalies and incomplete information. To deal with this issue, we introduce a new graph pattern matching (GPM) method, called partial simulation, capable of retrieving matches despite missing parts of the pattern graph, such as vertices and/or edges. Furthermore, considering the number and inequality of the outputs, we define a relevance function to compute a value expressing how each match vertex respects the pattern graph. Similarly, we define partial dual simulation GPM that returns vertices that satisfy a part of the dual simulation constraints and assigns a relevance value to them. Additionally, we provide distributed scalable algorithms to evaluate the proposed partial simulation methods based on the distributed vertex-centric programming paradigm. Finally, our experiments on real-world data graphs demonstrate the effectiveness of the proposed models and the efficiency of their associated algorithms.
Error analysis of multi-step day-ahead PV production forecasting with chained regressors

This paper presents a comprehensive error analysis of the day-ahead photovoltaic (PV) production multi-step forecasting model that uses a chained support vector regression (SVR). A principal component analysis (PCA) is also implemented to investigate possible improvements of the SVR prediction accuracy. Special attention was given to the hyper-parameter tuning of the chained SVR and PCA+SVR models; specifically, the dispersion of the prediction errors when fine-tuning the model with an experimental halving random search algorithm implemented within scikit-learn, i.e. the HalvingRandomSearchCV (HRSCV). The obtained results were compared with the traditional randomized search technique, i.e. the RandomizedSearchCV (RSCV). The chained SVR model prediction errors were analysed for several different parameter distribution settings. After doing repetitive fine-tuning and predictions, it was observed that the HRSCV tends to choose sub-optimal hyper-parameters for certain scenarios, as will be elaborated in the paper. Moreover, when analysing prediction errors of the same model fine-tuned repetitively with the HRSCV and RSCV, it was found that HRSCV creates larger errors and more inconsistency (variability) in the prediction results. The introduction of the PCA to the chained SVR model, at the same time, reduces the influence of exogenous variables and, on average, increases its performance and decreases prediction errors regardless of the optimization technique used.
Improving the Robustness of Loanword Identification in Social Media Texts

As a potential bilingual resource, loanwords play a very important role in many natural language processing tasks. If loanwords in a low-resource language can be identified effectively, the generated donor-receipt word pairs will benefit many cross-lingual natural language processing tasks. However, most studies on loanword identification mainly focus on formal texts such as news and government documents. Loanword identification in social media texts is still an under-studied field. Since it faces many challenges and can be widely used in several downstream tasks, more efforts should be put on loanword identification in social media texts. In this study, we present a multi-task learning architecture with deep bi-directional recurrent neural networks for loanword identification in social media texts, where different task supervision can happen at different layers. The multi-task neural network architecture learns higher-order feature representations from word and character sequences along with basic spell error checking, part-of-speech tagging, and named entity recognition information. Experimental results on Uyghur loanword identification in social media texts in five donor languages (Chinese, Arabic, Russian, Turkish, and Farsi) show that our method achieves the best performance compared with several strong baseline systems. We also combine the loanword detection results into the training data of neural machine translation for low-resource language pairs. Experiments show that models trained on the extended datasets achieve significant improvements compared with the baseline models in all language pairs.
Dynamic trajectory quantification strategy for multiple object tracking with feature rearrangement

End-to-end multiple object tracking (MOT) has been very popular recently in the field of computer vision. However, when performing the inter-frame data association, the majority of algorithms ignore the diversity among trajectories and treat them uniformly. In the joint-detection-and-tracking (JDT) paradigm, there exists an optimization conflict between detection and re-identification due to their different optimization directions. In this paper, we propose a dynamic trajectory quantification (DTQ) strategy for MOT with feature rearrangement. Initially, the DTQ strategy quantifies each trajectory to show its quality, and the score is renewed every frame. To adapt to different situations, we dynamically adopt different updating mechanisms. Additionally, we provide a channel-enhanced feature rearrangement module to alleviate the optimization conflict between subtasks in the JDT paradigm, which obtains more convincing detection results for the DTQ strategy. We evaluate our proposed tracker on several benchmarks, i.e., MOT15, MOT16, MOT17, and MOT20. Extensive experimental results demonstrate that our method achieves competitive results while still maintaining a sizable real-time tracking speed.
EA-POT: An Explainable AI Assisted Blockchain Framework for HoneyPot IP Predictions

The culpable cybersecurity practices that threaten leading organizations are logically prone to establishing countermeasures, including HoneyPots, and bestow research innovations in various dimensions such as ML-enabled threat predictions. This article proposes an explainable AI-assisted permissioned blockchain framework named EA-POT for predicting potential defaulters' IP addresses. EA-POT registers the predicted defaulters based on the suggestions levied by explainable AI and the approval of IP authorizers to blockchain database to enhance immutability. Experiments were carried out at IoT Cloud Research laboratory using three prediction models such as Random Forest Modeling (RFM), Linear Regression Modeling (LRM), and Support Vector Machines (SVM); and, the observed experimental results for predicting the AWS HoneyPots were explored. The proposed EA-POT framework revealed the procedure to include interpretable knowledge while blacklisting IPs that reach HoneyPots.
Exact bounds for acyclic higher-order recursion schemes

Beckmann [1] derives bounds on the length of reduction chains of classes of simply typed λ-calculus terms which are exact up-to a constant factor in their highest exponent. Afshari et al. [2] obtain similar bounds on acyclic higher-order recursion schemes (HORS) by embedding them in the simply typed λ-calculus and applying Beckmann's result. In this article, we apply Beckmann's proof strategy directly to acyclic HORS, proving exactness of the bounds on reduction chain length and obtaining exact bounds on the size of languages generated by acyclic HORS.
Robust extension of light fields with probable 3D distribution based on iterative scene estimation from multi-focus images

In this study, we propose an iterative approach to perform pseudo-scene reconstruction based on multi-focus images for estimating the 4D light fields of the scene robustly. An analogous scene-independent linear filtering approach has been proposed to efficiently achieve free viewpoint image reconstruction and scene refocusing with virtual bokeh by solely extracting 3D frequency components. However, it only succeeded in synthesizing limited regions of 4D light fields from multi-focus images, which can lead to awful degradation of reconstructed free viewpoint images outside the range due to the lack of the corresponding frequency components. In this paper, we directly utilize a linear relationship of 3D convolution combining multi-focus images and 3D scene by 3D blurring filters to estimate the 3D distribution itself of the scene instead of deriving the scene-independent linear filters from the relationship. In other words, we perform the scene reconstruction as a simplified convex optimization problem by modeling multi-focus images in multi-layered blurring composed of the scene and a bokeh filter. Algebraic reconstruction techniques of computed tomography, which uses the classical steepest descent method combined with projection onto convex sets (POCS) to ensure the convexity of the problem constraints, are introduced into such 3D scene reconstruction. The new method can be highly anticipated to assist us in extensively estimating robust 4D light fields. Experimental results illustrate that this approach enables good quality 3D image reconstruction with a wider range of 4D light fields. We also discuss the reduction of high computational costs and enormous memory requirements imposed by iterative algebraic reconstruction techniques. The quality of the obtained 4D light fields is comprehensively evaluated using both synthetic and real multi-focus images of practicable image resolution and depth range, additionally to an analysis of the faster processing times when comparing our iterative approach to the performance of the prior linear filters.
A CNN-based no reference image quality metric exploiting content saliency

Assessing the quality of images is a challenging task. To achieve this goal, images must be evaluated by a pool of subjects following a well-defined protocol or an objective quality metric must be defined. In this work, an objective quality metric based on deep neural network is proposed. The metric takes into account the human vision system by computing the saliency map and natural scene statistics features of the image under test. The neural network is composed by two modules: the convolutional layers and the regression units. The first one is trained by using preprocessed distorted images. The feature weights of the first module are smoothed by exploiting the estimated saliency map. The latter module is fit with the ground truth quality scores of the input image and the scaled feature weights obtained from first module by using visual sensitivity factor of image obtained using natural scene statistics features. The performances of the proposed metric have been evaluated by using four datasets: LIVEIQA, TID2013, CSIQ, and KADID10K. The achieved results show the effectiveness of the proposed system in closely matching the predicted quality scores with the ground truth ones.
From Ideal Worlds to Ideality

Abstract In common treatments of deontic logic, the obligatory is what is true in all deontically ideal possible worlds. In this article, I offer a new semantics for Standard Deontic Logic with Leibnizian intensions rather than possible worlds. Even though the new semantics furnishes models that resemble Venn diagrams, the semantics captures the strong soundness and completeness of Standard Deontic Logic. Since, unlike possible worlds, many Leibnizian intensions are not maximally consistent entities, we can amend the semantics to invalidate the inference rule which ensures that all tautologies are obligatory. I sketch this amended semantics to show how it invalidates the rule in a new way.
Seeking rhetorical validity in fear appeal research: An application of rhetorical theory

A significant focus of behavioral security research has been on understanding employees’ motives for protecting sensitive assets. To date, theorizing efforts in this space have focused on appraisal processes and nomological models that are designed to capture the responses to the security threats articulated within fear appeals. Because fear appeals are persuasive messages used to generate a sense of urgency about a specific threat that influences protective information security behaviors, they are an important focus in behavioral security research. However, we suggest that theoretical guidance is needed to facilitate further research in this area. We argue that ultimately, fear appeals are treatments intended to serve as catalysts of behavioral change. Therefore, fear appeals should have rhetorical validity, which is a specialized form of ecological validity in which contextualization has been included to ensure that the language embedded in the stimulus is consistent with the threat environment and the expectations of its audience. The rhetorical validity of a fear appeal should account for its exigence, the audience, and any constraints that shape the presentation and reception of the discourse. Using the illustrative example of a well-known behavioral security theory, protection motivation theory, this study provides a framework for behavioral security scholars in designing fear appeals that have rhetorical validity.
Optimal binning for a variance based alternative of mutual information in pattern recognition

Mutual information (MI) is a widely used similarity measure in pattern recognition. MI uses entropy as a measure of uncertainty to quantify the structural similarity of two vectors. Replacing entropy with variance as a measure of uncertainty, an analogous class of similarity measures can be derived and estimated by regression techniques. Recently, the non-linear piecewise constant regression (PWCR) has been proposed to drive similarity measures of this scheme, leading to competitive alternatives of MI. Although PWCR is based on binning, the optimal binning technique for certain problems remained an open question. In this paper, we show mathematically that the optimal binning needs to be aligned with the expected relationship between the vectors being compared. In general, approximately optimal binnings can be found by combinatorial optimization, and in certain cases the optimal binning can be determined by k-means clustering. The theoretical findings are supported by numerical experiments that show a 2–5% increase in the AUC scores in simulated pattern recognition scenarios and improved feature rankings in feature selection problems. The results suggest that the proposed binning techniques could improve the performance of PWCR-driven similarity measures in real-world applications.
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold

Optimization problems on the generalized Stiefel manifold (and products of it) are prevalent across science and engineering. For example, in computational science they arise in symmetric (generalized) eigenvalue problems, in nonlinear eigenvalue problems, and in electronic structures computations, to name a few problems. In statistics and machine learning, they arise, for example, in various dimensionality reduction techniques such as canonical correlation analysis. In deep learning, regularization and improved stability can be obtained by constraining some layers to have parameter matrices that belong to the Stiefel manifold. Solving problems on the generalized Stiefel manifold can be approached via the tools of Riemannian optimization. However, using the standard geometric components for the generalized Stiefel manifold has two possible shortcomings: computing some of the geometric components can be too expensive and convergence can be rather slow in certain cases. Both shortcomings can be addressed using a technique called Riemannian preconditioning, which amounts to using geometric components derived by a preconditioner that defines a Riemannian metric on the constraint manifold. In this paper we develop the geometric components required to perform Riemannian optimization on the generalized Stiefel manifold equipped with a non-standard metric, and illustrate theoretically and numerically the use of those components and the effect of Riemannian preconditioning for solving optimization problems on the generalized Stiefel manifold.
Analysis of topic popularity within a child sexual exploitation Tor hidden service

Knowledge about the online bulletin board communities dedicated to child sexual exploitation (CSE) located on the Darkweb (Tor) has generally been limited to those that investigate and prosecute individuals participating in those sites, as accessing them and viewing the abuse material is a crime. This leaves many of those in the CSE field without a scientifically validated foundation of the behaviors typically demonstrated by members of these CSE online communities. The following research empirically examines topic popularity of one Darkweb CSE Tor hidden service that was seized by a federal law enforcement agency. Analyses reveal that some topics tend to be viewed significantly more often, and descriptors about specific sexual acts and the ages of children portrayed in the thread titles correlate with the frequency of viewing, approving and downloading of that material. Implications of these findings for professionals working in the CSE field will be discussed.
Solar Energy Potential Assessments in Rain-Dominated Tropical Monsoon Climates

This chapter is intended to give an overview of solar energy potential of regions under monsoon climate.
Slide decks as government publications: exploring two decades of PowerPoint files archived from US government websites

Over the last three decades, US government agencies have published hundreds of thousands, if not millions, of PowerPoint files on the web. Hundreds of thousands of these files have been captured and preserved in web archives. With that noted, it remains difficult to find and interact with these files. This paper analyzes a public dataset of 1,000 PowerPoints from US government websites in the Library of Congress web archives to explore the properties of these kinds of files. This publicly available dataset published in 2019 includes a random sample of a thousand files from the more than 300,000 files that purport a PowerPoint media type in the Library of Congress web archives. The study focuses on characterizing the nature of these publications, the extent to which embedded metadata in these documents could be used to improve access to them and exploring what properties of these files are likely to be important to future users. Exploring these data provides a means to begin to understand the value and nature of PowerPoint files as a format of government publishing and government records.
Multiple discriminant preserving support subspace RBFNNs with graph similarity learning

In a high-dimensional sample space, the distribution characteristics of samples are complex. It is difficult to accurately describe the local distribution characteristics of samples directly in the original sample space. This paper proposes a multiple discriminant preserving support subspace radial basis function neural networks with graph similarity learning (DPSS-RBFNN) to describe the distribution characteristics of samples in the original high-dimensional space by multiple low-dimensional and simple discriminant preserving subspaces. DPSS-RBFNN includes the discriminant preserving support subspace (DPSS) learning module and the subspace distribution feature extraction (SDFE) module. In the DPSS learning module, the discriminativeness of each attribute and the joint discriminant between attributes are first considered to construct multiple subspaces. The discriminativeness of the features in these subspaces is not lower than that of the original samples. The graph similarity learning is used to measure the similarity of sample distributions between subspaces with different dimensions. Then multiple DPSSs are obtained. In SDFE module, the distribution characteristics of the samples are described by combining the local responses of the feature space extracted by the sub-RBFNN in each DPSS. The experimental results show that the proposed DPSS-RBFNN with few kernels can achieve higher accuracy in the recognition task than state-of-the-art algorithms.