Reviewerly/exHarmony huggingface dataset:
Time-Varying Transmission Effects of Internet Finance Under Economic Policy Uncertainty and Internet Consumers’ Behaviors: Evidence from China

The modern finance industry is composed of not only numerous financial intermediaries but also internet-based mechanisms which are operated by mobile phone users and online consumers daily. In the coming 10 years, estimates suggest that over half of banks’ functions will likely be replaced by high-tech artificial intelligence. Given the great ongoing shifts in contemporary financial systems, the transmission effects of internet-based finance practices have introduced an important yet unaddressed empirical question on the coupling relationship between the internet finance industry and economic policy uncertainty (EPU). This paper adopts the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model and novel data from Alibaba Corp. to investigate this relationship. We find: First, the impact of internet-based financial approaches on EPU is greater than the reversal effect, indicating that China’s gross domestic product (GDP) is largely influenced by the online finance industry. Second, the lag impacts are time varying and become stable after 2016, corresponding to the current Chinese government’s long-term strategic plan that emphasizes maintaining the economy’s overall stability. Lastly, additional evidence shows that the online financial approaches are positively correlated with consumers’ behaviors, implying that the online finance industry is gaining its momentum when people are using e-currency rather than real cash. After all, it takes time to observe the real effect of these macro policies. With internet and information technology developing, artificial intelligence is being used in the areas of big data, credit lending, and risk control. This largely reduces the data analyzing cost for internet finance companies and makes risk control more convenient.
The Search for Symmetry in Hohfeldian Modalities

In this work we provide an analysis of some issues arising with geometrical representations of a family of deontic and potestative relations that can be classified as Hohfeldian modalities, traditionally illustrated on two diagrams, the Hohfeldian squares. Our main target is the lack of symmetry to be found in various formal accounts by drawing analogies with the square of opposition for alethic modalities. We argue that one should rather rely on an analogy with the alethic hexagon of opposition and exploit the notions of contingency and absoluteness in order to restore the symmetry of Hohfeldian modalities in accordance to the diagrams presented by Hohfeld. Interestingly, the investigation unveils three potestative squares defined at different levels of granularity (force, outcome and change) and allows us to further elaborate on the connections between deontic and potestative relations.
Companion Losses for Deep Neural Networks

Modern Deep Neuronal Network backends allow a great flexibility to define network architectures. This allows for multiple outputs with their specific losses which can make them more suitable for particular goals. In this work we shall explore this possibility for classification networks which will combine the categorical cross-entropy loss, typical of softmax probabilistic outputs, the categorical hinge loss, which extends the hinge loss standard on SVMs, and a novel Fisher loss which seeks to concentrate class members near their centroids while keeping these apart.
OCR Improvements for Images of Multi-page Historical Documents

This work presents a pipeline for processing digitally scanned documents, reading their textual content, and storing it in a dataset for the purpose of information retrieval. The pipeline is able to handle images of various quality, whether they were obtained by a digital scanner or camera. The image can contain multiple pages in any layout, but an approximate upright orientation is assumed. The pipeline uses Faster R-CNN to detect individual pages. These are then processed by a deskew algorithm to correct the orientation, and finally read by the Tesseract OCR system that has been retrained on a large set of synthetic images and a small set of annotated real-world documents. By applying the pipeline, we were able to increase the word recall to 60.56% which is an absolute gain of 19.19% from the baseline solution that uses only Tesseract OCR. A demo of the proposed pipeline can be found at https://archivkgb.zcu.cz/.
Transformation of Strategic Models for Managing Human Risks of Information Security of an Enterprise as an Imperative of the Digital Industry

This article substantiates the imperatives of transforming the information security (IS) human risk management model at a digital industry enterprise using the theories of strategic management, psychological ownership (involvement), and cultural parameters of human activity. The types of strategies and strategic models of information security culture (ISC) have been substantiated. With the use of sociological research, the dominance in organizations of the ISC defensive strategy was revealed, the pattern of the transition from the defensive to the developing strategic ISC model was revealed, and then the transition to the integrative ISC management strategy, thus combining both strategic models. The concept of the draft Information security culture national standard, which can be the basis for the design and implementation of a standard of the same name for any enterprise, is presented.
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis

Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important applications such as assisting in medical training. In this work, we leverage the efficient self-attention and contrastive learning modules and build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN, which can generate high-quality histopathology images based on multi-attribute inputs. In comparison to existing single-attribute conditional generative models, our proposed model better reflects input attributes and enables smoother interpolation among attribute values. We conduct experiments on a histopathology dataset containing stained H&E images of urothelial carcinoma and demonstrate the effectiveness of our proposed model via comprehensive quantitative and qualitative comparisons with state-of-the-art models as well as different variants of our model.
Immersed in Pop

In 2019, Dolby Atmos Music launched on Tidal HiFi and Amazon Prime Music HD, integrating 3D sound with music streaming services for the first time. This is one of several recent incursions by popular music into immersive and interactive mediums, which also includes technologies such as virtual reality and 360-degree video. As immersive productions become more normal in popular music, they are increasingly critical to examine. How are aesthetic features of pop compositions altered or maintained in these productions? And how do these different spatial mediums affect compositional design, subject positioning, artists’ performativity, and staging? This article aims to address these questions by presenting a model for 3D music analysis that relates various notions of music technology and production to musicological concepts on performance environment, staging, and subject position. This model is then demonstrated in a hermeneutic close reading of the song ‘Blinding Lights’ by R&amp;B superstar The Weeknd, which was released in both stereo and Dolby Atmos mediums in 2019. The goal of the analysis is to demonstrate some of the ways different mediums impinge on various interpretive stances and the relationship between the performer and listener in immersive popular music.
Equivalence regimes for geometric quantum discord and local quantum uncertainty

The concept of quantum discord aims at unveiling quantum correlations that go beyond those described by entanglement. Its original formulation [J. Phys. A 34, 6899 (2001); Phys. Rev. Lett 88, 017901 (2002)] is difficult to compute even for the simplest case of two-qubits systems. Alternative formulations have been developed to address this drawback, such as the geometric measure of quantum discord [Phys. Rev. A 87, 062303 (2013)] and the local quantum uncertainty [Phys. Rev. Lett 110, 240402 (2013)] that can be evaluated in closed form for some quantum systems, such as two-qubit systems. We show here that these two measures of quantum discord are equivalent for 2 x D dimensional bipartite quantum systems. By considering the relevant example of N00N states for phase estimation in lossy environments, we also show that both metrics of quantum discord quantify the decrease of quantum Fisher information of the phase estimation protocol. Given their ease of computation in 2 x D bipartite systems, the geometric measure of quantum discord and the local quantum uncertainty demonstrate their relevance as computable measures of quantum discord.
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images

This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are replaced by prototypical networks for online adaptation to new classes. These networks produce embeddings vectors for each generated box, which are then compared with class prototypes. The distance between an embedding and a prototype determines the corresponding classification score. The networks are trained in an episodic manner. A new detection task is randomly sampled at each epoch, consisting in detecting only a subset of the classes annotated in the dataset. This strategy encourages the network to adapt to new classes as it would at test time. In addition, several ideas are explored to improve the proposed method such as a hard negative examples mining strategy and self-supervised clustering for background objects. The performance of our method is assessed on DOTA, a large-scale remote sensing images dataset. The experiments conducted provide a broader understanding of the capabilities of representation learning. It highlights in particular some intrinsic weaknesses for the few-shot object detection task. Finally, some suggestions and perspectives are formulated according to these insights.
Optimized Power Normalized Cepstral Coefficients Towards Robust Deep Speaker Verification

After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification. However, as a feature extractor with long-term operations on the power spectrogram, its temporal processing and amplitude scaling steps dedicated on environmental compensation may be redundant. Further, they might suppress intrinsic speaker variations that are useful for speaker verification based on deep neural networks (DNN). Therefore, in this study, we revisit and optimize PNCCs by ablating its medium-time processor and by introducing channel energy normalization. Experimental results with a DNN-based speaker verification system indicate substantial improvement over baseline PNCCs on both in-domain and cross-domain scenarios, reflected by relatively 5.8% and 61.2% maximum lower equal error rate on VoxCeleb1 and VoxMovies, respectively.
Digital color-image encryption scheme based on elliptic curve cryptography ElGamal encryption and 3D Lorenz map

Now a days, The security of digital data is an important task. In this paper, we propose a novel method of digital color-image encryption and decryption based on elliptic curve cryptography (ECC) ElGamal encryption and 3D Lorenz map. In this proposed method, we have applied ECC on the digital color-image, for this, a mapping method is proposed to convert the pixels of the image as points on the elliptic curve. Further, to enhance the security of the cryptosystem, a substitution method is applied in the intermediate image using the keystream generated by the 3D Lorenz map. The decryption method is the same but in reverse order. A mapping inverting method is proposed for the decryption to get the original image. The security and statistical analyses are done on various images and the experimental results show the robustness of the proposed method.
Reverse Firewalls for Adaptively Secure MPC Without Setup

We study Multi-party computation (MPC) in the setting of subversion, where the adversary tampers with the machines of honest parties. Our goal is to construct actively secure MPC protocols where parties are corrupted adaptively by an adversary (as in the standard adaptive security setting), and in addition, honest parties' machines are compromised.
PENGARUH BIMBINGAN BELAJAR ORANGTUA TERHADAP DISIPLIN BELAJAR SISWA SEKOLAH DASAR

The purpose of this study was to determine the effect of parental tutoring on the learning discipline of elementary school students in group, Kelimutu sub-district. The approach in this research is done quantitatively with the research method is correlational research. The research population was all elementary school students in group 1, Kelimutu sub-district, Ende district, as many as 432 students with a purposive sampling technique, so that the sample in this study were students in grades IV-VI of elementary school group 1, Kelimutu district, totaling 80 people. The data collection method used a questionnaire, namely a parent learning guidance questionnaire and a student learning discipline questionnaire. Analysis of research data was carried out to test descriptive statistics, test data normality and test data linearity, as well as test hypotheses. The results showed that the value of the regression equation Y=4.677+0.931 indicates the direction of the regression equation is positive. The value of F count = 325.726 &gt; F table = 3.96 with a significance value of 0.000 &lt;0.05, meaning that there is a significant effect of the parental tutoring variable on the student learning discipline variable. The coefficient of determination test obtained an Rsquare value of 0.807 or 80.7%, meaning that student learning discipline is influenced by parental tutoring with a magnitude of influence of about 80.7% showing a very strong influence while the rest is influenced by other factors. In conclusion, the higher the parental guidance, the higher the student's learning discipline, on the contrary, the lower the parental guidance, the lower the student's learning discipline.
Modern Russian University: Key Models Of Development In Era Of Globalization

An article sets the problem of higher professional Russian education entering the transnational globalizing world. It is stated that in the 21st century the architecture of the global educational space in general and the educational landscape of higher education institutions development in particular are drastically changing. It is affirmed that the university, being the creator of the intellectual capital, becomes the provider of higher education in the world educational space in the 21st century. The article identifies the main prerequisites for the emergence of innovative models of the university development in the 21st century. The key models of universities in the context of the era of globalization are revealed. Based on the analysis of the functioning of a number of universities the authors justify the model of the university development, which can be considered relevant and appropriate in the context of current and foreseeable realities of the 21st century. A universal type of a successful modern Russian university, called “the fourth- generation university”, is defined and its main characteristics are summarised. The most promising approach to training specialists for the Russian digital economy is justified.
Specificity-preserving RGB-D saliency detection

Abstract Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. Specifically, we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and propagate the fused feature to the next layer to integrate cross-level information. Moreover, to capture rich complementary multi-modal information to boost SOD performance, we use a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using skip connections between encoder and decoder layers, hierarchical features can be fully combined. Extensive experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at https://github.com/taozh2017/SPNet .
Generating Analytics from Web Log

Recent engineering trends integrate clever expertise in every factors of our lifes. Today’s technologies generate terra bytes of recorded messages every day to record their data. It is difficult to research on such recorded messages and represent usable records such as patterns to directors, so as to manipulate and reveal those technology. Patterns minimally characterize huge corporations of recorded messages and allow the manager to do future analysis of data, along with variance detection and event forecast. Even though patterns exist typically in automatic recorded messages, spotting them into large collection of recorded messages from different resources lacking any past information is a widespread responsibility. We aim a bigdata using hadoop so as to extract very pleasant styles for the set of recorded messages which are given. Our approach is high-speed, memory efficient, exact, and expandable. Hadoop is implemented in map reduce framework intended for disbursed platforms to procedure hundreds of thousands of recorded messages in a moment. Hadoop is a strong technology which is used for different recorded messages produced in a wide style of systems. Present technique uses algorithmic procedures to limit the additional over- head based totally on the truth that recorded messages are continually routinely produced. We examine the accuracy of Log Mine of huge units of recorded messages produced in commercial appliances. It has efficiently created styles which might be as exact as the styles produced by genuine and un expandable procedures.
PSYCHOLOGICAL MODEL OF CIVIC IDENTITY FORMATION

Aim. The research aims to present the psychological concept of civic identity formation, namely author's view on the nature of civic identity, the stages and psychological mechanisms of civic identity formation. Methods. A system of general scientific methods was used: analysis and synthesis of the main provisions of research sources on the study of civic identity as a complex multidimensional personality formation, comprehension of the psychological foundations of civic identity, systematization and generalization of scientific provisions on the stages and mechanisms of identity. Results. It is proposed to consider civic identity as a kind of organizational identity, which is self-determination in the organizational environment of the state, as well as self-identification with the role of a citizen and is the primary psychological regulator of civic behavior. The main stages of civic identity formation are: perceptual-systemic, normative-community and individual-integrational. The main mechanisms of formation of civic identity are: internalization of stereotypes and attitudes of citizenship; imitation of models of civic behavior; individuation of the meanings (sense) of citizenship; exteriorization of stereotypes, attitudes and patterns of civic behavior; implementation of the experience of interaction with the state and fellow citizens; comprehension of one's own citizenship. Conclusions. Formation of civic identity (civic protoidentity – reproductive civic identity – productive civic identity) is a dynamic process that has staged nature as it involves a series of successive stages, at each of which civic identity undergoes qualitative changes and is formed as an individual's awareness and reflection of his/her place, role and degree of activity in the system of interaction with other citizens and the state.
An Ising Machine-Based Solver for Visiting-Route Recommendation Problems in Amusement Parks

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.
No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis

Abstract Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning to reach strong attack performance, which can be far from trivial. Besides many options stemming from the machine learning domain, recent years also brought neural network elements specially designed for side-channel analysis. The loss function, which calculates the error or loss between the actual and desired output, is one of the most important neural network elements. The resulting loss values guide the weights update associated with the connections between the neurons or filters of the deep learning neural network. Unfortunately, despite being a highly relevant hyperparameter, there are no systematic comparisons among different loss functions regarding their effectiveness in side-channel attacks. This work provides a detailed study of the efficiency of different loss functions in the SCA context. We evaluate five loss functions commonly used in machine learning and three loss functions specifically designed for SCA. Our results show that an SCA-specific loss function (called CER) performs very well and outperforms other loss functions in most evaluated settings. Still, categorical cross-entropy represents a good option, especially considering the variety of neural network architectures.