Reviewerly/exHarmony huggingface dataset:
Virtual/Augmented Realities, Haptics, and Problems of Ontology

In this article, virtual reality (VR) and augmented reality (AR) are analysed from the standpoint of digital materialism, which states that any digital matter (including VR and AR) is an in-material construct. The authors describe VR and AR as special ontological models. It is emphasized that the basis for the creation and development of VR and AR is the sense of touch, which, in its turn, is a fusion of sensations, including optic ones. Moreover, the authors consider the hand to be the source of and a metaphor for VR and AR: it is the main organ of the human body responsible for touch and, therefore, determines haptics as a fundamental mode of comprehension of regular reality. Thus, the concept of entelechy introduced by Aristotle can serve as a key to understand how VR and AR are implemented and presented. If, according to Aristotle, the soul as a hand is the entelechy of a natural body, then a hand in a cyber glove, which makes staying in VR possible, is the entelechy of a body converged with the electronic and digital environment. In the case of AR, a hand does not necessarily require technogarments and turns out to be the entelechy of a non-technological body immersed, with its help, into the electronic and digital environment. As a result, VR and AR themselves become the entelechies, that is, tangible, or, in other words, haptically perceived forms and images of being. The authors conclude that both VR and AR depend on human experience and are ontologically irresponsible. Allowing a person to comprehend his/her own existence in the affordances bestowed upon him/her, VR and AR demonstrate how the human body reacts to the work of the brain immersed into the virtual and augmented worlds.
Robustness against Relational Adversary

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation need not be bounded by small lp-norms. Motivated by the semantics-preserving attacks in vision and security domain, we investigate relational adversaries, a broad class of attackers who create adversarial examples that are in a reﬂexive-transitive closure of a logical relation. We analyze the conditions for robustness and propose normalize-and-predict – a learning framework with provable robustness guarantee. We compare our approach with adversarial training and derive an uniﬁed framework that provides beneﬁts of both approaches. Guided by our theoretical ﬁndings, we apply our framework to malware detection and image classiﬁcation. Results of both tasks show that attacks using relational adversaries frequently fool existing models, but our uniﬁed framework can signiﬁcantly enhance their robustness.
Fault Recovery System For Smart Grid Based On Machine Statistical Learning

In order to overcome the problems of poor robustness, low-accuracy and long time-consuming when traditional system recovers power grid faults, a fault recovery system based on machine statistical learning is designed. The system framework consists of sensing layer, network layer and application layer. Through the overall framework of the system, the hardware of the system is designed, including data acquisition device block, transmission device, analysis module and display device. In the software part, fault acquisition subroutine, fault location subroutine and fault type identification subroutine are designed to obtain accurate fault data. Finally, machine statistical learning method is used to complete the design of fault recovery subroutine of smart grid, recover the obtained fault data and realise the design of fault recovery system of smart grid. The results show that the robustness, accuracy and time-consumption of the system are improved, and the problems existing in the traditional system are solved.
Practical Lattice-Based Zero-Knowledge Proofs for Integer Relations.

We present a novel lattice-based zero-knowledge proof system for showing that (arbitrary-sized) committed integers satisfy additive and multiplicative relationships. The proof sizes of our schemes are between two to three orders of magnitude smaller than in the lattice proof system of Libert et al. (CRYPTO 2018) for the same relations. Because the proof sizes of our protocols grow linearly in the integer length, our proofs will eventually be longer than those produced by quantum-safe succinct proof systems for general circuits (e.g. Ligero, Aurora, etc.). But for relations between reasonably-sized integers (e.g. $512$-bit), our proofs still result in the smallest zero-knowledge proof system based on a quantum-safe assumption. Of equal importance, the run-time of our proof system is at least an order of magnitude faster than any other quantum-safe scheme.
The Impact of the New Crown Epidemic in 2020 on Online Education

The novel Coronavirus outbreak in early 2020 disrupted people's normal life and caused many middle and primary schools and universities in China to postpone their return to school one after another. The central government called on all places to implement the policy of suspending classes and never stopping school. All of a sudden, online education has exploded. This paper focuses on the comparison of the development overview of online education around 2020, the growth trend and causes of online education, and the data analysis of the blowout growth of online education during the epidemic period, and predicts the future development trend of online and offline education.
Danish Similarity Data Set (date: Nov. 18, 2019)

The Danish similarity dataset is a gold standard resource for evaluation of Danish word embedding models. The dataset consists of 99 word pairs rated by 38 human judges according to their semantic similarity, i.e. the extend to which the two words are similar in meaning, in a normalized 0-1 range.Note that this dataset provides a way of measuring similarity rather than relatedness/association.Description of files included in this material:(Note: In both of the included files, rows correspond to items (word pairs) and columns to properties of each item.)All_sims_da.csv: Contains the non-normalized mean similarity scores over all judges, along with the non-normalized scores given by each of the 38 judges on the scale 0-6, where 0 is given to the most dissimilar items and 6 to the most similar items.Gold_sims_da.csv: Contains the similarity gold standard for each item, which is the normalized mean similarity score for a given item over all judges. Scores are normalized to a 0-1 range, where 0 denotes the minimum degree of similarity and 1 denotes the maximum degree of similarity.
The testing of WDF abstracts

When we use special characters like: &lt;, &gt;, &amp;, *, or $, we want to ensure that our submissions will be successful 100% of the time. Anything less than that will be an issue [and we don't want that; do we?]
DualTKB: A Dual Learning Bridge between Text and Knowledge Base

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality transfers. We examine different model architectures, and evaluation metrics, proposing a novel Commonsense KB completion metric tailored for generative models. Extensive experimental results show that the proposed method compares very favorably to the existing baselines. This approach is a viable step towards a more advanced system for automatic KB construction/expansion and the reverse operation of KB conversion to coherent textual descriptions.
Self-regulating neural mechanisms for self-motion estimation from optic flow

Humans are capable of accurately perceiving self-motion direction in many different environments, ranging from the real world to virtual environments to minimal random-dot scenes commonly used in psychophysical experiments. Neural models of heading perception are less adaptive, typically relying on parameters tuned to accommodate a narrow range of experimental conditions. In the present study, we build upon the competitive dynamics model of primate brain areas MT and MST (Layton & Fajen, 2016) so that it generates robust heading estimates from optic flow in a broad range of scenes, while automatically regulating key parameters that previously needed to be set by hand. In model area MT, speed-cell tuning curves needed to be manually configured to properly encode the range of optic flow speeds, which can vary widely with changes in environmental structure, self-motion speed, and eyeheight. We adapted the principles of efficient sensory encoding (Simoncelli & Ganguli, 2014) with a temporal component that allows speed cells to dynamically adjust to the distribution of optic flow speeds recently detected by the observer. Manual parameter selection was also required in model area MSTd to properly modulate competition between cells, which balances the stability of heading perception against responsiveness to true changes in heading. One way the visual system could achieve such flexibility across environments is via neural mechanisms that self-regulate the feedback and competition in MSTd. We implemented such a mechanism, using a weighted combination of template cell activities with differing decay rates and competitive dynamics to regulate the recurrent signal. Through model simulations using video from real-world and virtual scenes, we demonstrate how these changes enable flexible adaptation across a range of environments with accuracy similar to that achieved with manually selected parameters.
Musical local lore as a resource for patriotic education in the professional instrumental training class

We consider the aspect of students’ patriotic education in the professional instrumental training class on the basis of a selection of the most relevant for the pedagogical process meaning of the concept “patriotism” and the use of information from the field of musical local lore. The need to include in the pedagogical process intellectual conversations on patriotism as a subject of philosophy, political science is justified by a brief analysis of the existence in the Russian cultural environment of a popular quote from the heritage of S. Johnson. The concept of “patriotism” is comprehended in accordance with the definition of the German philosopher M. Merton, as well as in accordance with the works of I. Vernadsky, B. Chicherin and others. The main thesis being proved is that patriotism is not only a goal, but also a mean of education. The declared pedagogical position is based on the experience of patriotic education in domestic pedagogy, presented by the works of K.D. Ushinsky, V.P. Vakhterov, N.A. Korf, N.I. Pirogov, L.N. Tolstoy, P.P. Blonsky, P.F. Kapterev and others. Intellectualization in the “teacher-student” dialogue space is used as the main technique for the implementation of patriotic education. A selection of works recommended for inclusion in the repertoire of an instrumental musician is presented. The theoretical attitude is confirmed by examples of musicians’ works, whose names are cultural symbols of the Tambov Region: A.N. Verstovsky, S.V. Rachmaninov, F.I. Chaliapin, V.I. Agapkin, I.A. Shatrov. Particular emphasis is placed on the theme of the Great Patriotic War in the work of the modern Tambov composer – O.I. Egorova.
Bit Security Estimation Using Various Information-Theoretic Measures.

In this paper, we propose various quantitative information-theoretic bit security reduction measures that correlate the statistical difference between two probability distributions with the security level gap for two cryptographic schemes. We derive tighter relations between information-theoretic measures for quantifying the precision and guarantee the security level of the cryptographic scheme implemented over a precision-restricted environment. Further, the generalized versions of previous security reductions are devised by relaxing the constraints on the upper bounds of the information-theoretic measures. This makes it possible to estimate bit security more reliable and improves the security level. We also estimate the effects on the security level when the $\kappa $ -bit secure original scheme is implemented on a $p$ -bit precision system. In previous studies, $p$ was fixed as $\frac {\kappa }2$ ; however, the proposed schemes are generalized such that the security level $\kappa $ and precision $p$ can vary independently. This results in a significant difference. Moreover, previous results cannot provide the exact lower bound of the security level for $p\ne {\frac {\kappa }{2}}$ . However, the proposed results can provide the exact lower bound of the estimation value of the security level as long as the precision $p$ satisfies certain conditions. We provide diverse types of security reduction formulas for the six types of information-theoretic measures. The proposed schemes can provide information-theoretic guidelines regarding the difference between the security levels of two identical cryptographic schemes when extracting randomness from two different probability distributions. In particular, the proposed schemes can be used to quantitatively estimate the effect of the statistical difference between the ideal and real distributions on the security level.
China’s Digital Currency Revolution and Implications for International Business Strategy

As if Covid-19 and the trade war hasn’t done enough to upend global business between the US and China, yet another major transformation is afoot: the launch of China’s sovereign (i.e., central-bank offered) digital currency (CBDC). By “digital currencies” we mean financial transactions that are recorded on blockchain ledger and held in digital wallets. While Facebook’s Libra and other digital currency firms have received increasing attention, private firms are in fact unlikely to truly dominate the digital currency trade. Instead, sovereign digital currency – namely, the “Digital Currency Electronic Payments” (DCEP) system – managed by the People’s Bank of China (PBoC), China’s central bank, has taken the clear lead in this space. In this article, we briefly discuss why and how China’s DCEP is leading the digital currency revolution and what this means for international business.
How Do Employees Learn Security Behavior? An Integrated Perspective on Social Learning and Rational Decision Making

The information security has become one of the most important topics in the modern information technology of companies. It influences the way companies work and the exchange of information between them. Information security policies are one of the most important instruments for compliance with information security (ISC). It is particularly important that the defined rules are adhered to. In order to explain human behaviour with regard to ISC, this paper uses the established theory of Social Learning Theory (SLT) and Rational Choice Theory (RCT). However, they are rarely used to explain the ISC. This article aims to combine behavioral and IS research to better understand ISC. We provide an overview and description of the effects of SLT and RCT on ISC through a PLS analysis. The results of this study show that SLT has an influence on RCT and therefore the ISC can be explained by the two theories used.
Super-Resolution Method of Face Image using Capsule Network

In recent years, super-resolution using deep learning has attracted attention. Super-resolution is a technology for converting low-quality images to high-quality images. Super-resolution can be applied to the technology to identify a criminal from the video of a security camera. It is difficult to identify the criminal from raw images, because security cameras are low image quality to record long-term images. In this study, we propose a method to super-resolution human face images using Capsule network. Capsule Network represents input values and output values as vectors, which makes it possible to learn features between a positional relationship and an orientation of faces. Therefore, it can be expected to generate a face image of higher quality than Convolutional Neural Network (CNN). We employ the CelebA data set,which is collected about 200,000 face images, as the training data. The low quality image is generated from the original CelebA image. Capsule network is trained using original high quality images as outputs and low quality images as inputs. Experimental results show that the super-resolution method using capsule network generates high quality face image than CNN.
A Usability Study on Game Development using University Curriculum

This study is analytical prototype and usability studies centering on game development for game major in university curriculum by the collaboration of professor and students. The study set up the assumption of successful game development throughout the school curriculum initialized by fifteen weeks of time period. In the previous study, we defined the responsibility of positional personnel, and possibilities of current game majored school from both USA and Korea. The study aimed to analyze the productional guideline for the game development courses by conducting a usability survey from the groups of potential participants.
Using Dynamic Time Warping to Detect Clones in Software Systems

Software clone detection is a widely researched area over the last two decades. Code clones are fragments of code judged similar by some metric of similarity. This paper proposes an approach for code clone detection using dynamic time warping technique (i.e., DTW). DTW is a well-known algorithm for aligning and measuring similarity of time series and it has been found effective in many domains where similarity plays an important role such as speech and gesture recognition. The proposed approach finds clones in three steps. First software modules are extracted. Then, the extracted modules are turned to time series. Finally, the time series are compared using the DTW algorithm to find clones. The results of the experiment conducted on a well-known Benchmark show that the approach can detect clones effectively in software systems.
Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Abstract Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&amp;E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier . Author summary Breast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&amp;E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.
Predicting Classification Accuracy When Adding New Unobserved Classes.

Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier's performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. For this, we define a measure of separation between correct and incorrect classes that is independent of the number of classes: the reversed ROC (rROC), which is obtained by replacing the roles of classes and data-points in the common ROC. We show that the classification accuracy is a function of the rROC in multiclass classifiers, for which the learned representation of data from the initial class sample remains unchanged when new classes are added. Using these results we formulate a robust neural-network-based algorithm, CleaneX, which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes. Unlike previous methods, our method uses both the observed accuracies of the classifier and densities of classification scores, and therefore achieves remarkably better predictions than current state-of-the-art methods on both simulations and real datasets of object detection, face recognition, and brain decoding.