Reviewerly/exHarmony huggingface dataset:
SignSynth: Data-Driven Sign Language Video Generation

We present SignSynth, a fully automatic and holistic approach to generating sign language video. Traditionally, Sign Language Production (SLP) relies on animating 3D avatars using expensively annotated data, but so far this approach has not been able to simultaneously provide a realistic, and scalable solution. We introduce a gloss2pose network architecture that is capable of generating human pose sequences conditioned on glosses. (For sign languages a gloss is a written representation that describes a specific sign.) Combined with a generative adversarial pose2video network, we are able to produce natural-looking, high definition sign language video. For sign pose sequence generation, we outperform the SotA by a factor of 18, with a Mean Square Error of 1.0673 in pixels. For video generation we report superior results on three broadcast quality assessment metrics. To evaluate our full gloss-to-video pipeline we introduce two novel error metrics, to assess the perceptual quality and sign representativeness of generated videos. We present promising results, significantly outperforming the SotA in both metrics. Finally we evaluate our approach qualitatively by analysing example sequences.
Hierarchical Autoregressive Modeling for Neural Video Compression

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods.
Confidence in digital money: Are central banks more trusted than age is matter?

The virtual nature of digital money is fueling the conflict between usability, functionality and trust in the digital form. Institutional trust drivers should move forward in understanding the nature of confidence in digital money. Do central banks digital money (CBDC – central bank digital currency) and private cryptocurrencies demonstrate the same or different trust patterns? The paper used the general regression method to discover the relationship between trust in different forms of digital money and selected variables that may generate this trust. Simple empirical tests were sufficient to find the fundamental importance of age as a confidence driver relevant to CBDC and cryptocurrencies. It is found that traditional factors associated with the inflation history and quality of monetary order (central banks independence and rule of law) do not play a role in the case of CBDC, but are important in the case of cryptocurrencies. Structural features (like FinTech development or social trust) that should support trust in digital money are not found to be important. Societies with larger fraction of younger generations demonstrate higher confidence in centralized and decentralized forms of digital money. This challenges the traditional approach to money and calls into question the future role of monetary stability institutions in the digital age. Digitalization is perceived as an improvement in welfare only when fiat money institutions become fragile. The efficiency and credibility of central banks are not a bonus to confidence in CBDC. This is a challenge for the institutional design of the future digital-based monetary order.
Diversity Measures for Set-Based Meta-Heuristics

The diversity of agents moving through a search space gives researchers insight into a heuristic's ability to explore and exploit the search space. Much of the work done on measuring diversity has focused on heuristics for continuous search spaces. Due to the lack of spatial structure among sets, these established diversity measures are not applicable to set-based algorithms. Therefore, there is a need for setbased diversity measures that give researchers the same insights for set-based heuristics as the diversity measures for continuous-based heuristics. This paper conducts a thorough investigation into the use of the Jaccard distance and the Hamming distance as methods for calculating the diversity of set-based populations. It is shown that the Jaccard distance better represents a researcher's intuition about the diversity for sets, while Hamming distance under-represents diversity. In cases where the sizes of sets are relatively small, changes in diversity calculated using the Jaccard distance are large and give impressions of chaotic movements.
Post-Quantum Group-Oriented Authentication in IoT

The Internet of Things may include sensors, actuators, analyzers, logical controllers, which together form distributed network of heterogeneous devices. Group-based approach to authentication may be relevant when the number of nodes is very large and there are constrained devices among them. Post-quantum schemes as candidates in NIST competition are considered. Lattices are chosen as best candidates for building group-oriented schemes for IoT due to high performance, relatively small key sizes, well researched mathematical problem. To give flexibility to the lattice-based group-oriented authentication protocols, the basis delegation mechanism was considered as an approach that takes into account the hierarchy in the Internet of Things systems.
Nonparametric clustering for image segmentation

Abstract Image segmentation aims at identifying regions of interest within an image by grouping pixels according to their properties. This task resembles the statistical one of clustering, yet many standard clustering methods fail to meet the basic requirements of image segmentation since the identified segments are often biased toward predetermined shapes and their number is rarely determined automatically. Nonparametric clustering is, in principle, free from these limitations and particularly suitable for the task of image segmentation. We discuss the application of nonparametric clustering to image segmentation and provide an algorithm specific for this task. Pixel similarity is evaluated in terms of the density of the color representation. The adjacency structure of the pixels is exploited to introduce a simple, yet effective method to identify image segments as disconnected high‐density regions. The proposed method answers to the need of both segmenting an image and detecting its boundaries and can be seen as a generalization to color images of the class of thresholding methods.
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5\% clean training data without causing obvious performance degradation on clean examples. Code is available in https://github.com/bboylyg/NAD.
Core Trends in Engineering Education: Five Years of the “Synergy” International Conference

This article discusses the topical issues of engineering education, which have become the discussion point at the international network scientific and practical conference titled “Synergy” that has been held since 2016. The organizational basis of the conference is provided by the leading technical universities in Russia under the support of the international societies for engineering education and the largest Russian energy company Gazprom. The article outlines the broad issues of the conference, related to interdisciplinarity, new standards and technologies for engineering education, digital educational environment and online technologies, interaction between engineering education and high-tech business and industry, the models of digital competencies and mechanisms to independently certify them, training of highly qualified personnel, professional education within the school-university-enterprise system, and many other relevant aspects of contemporary engineering education.
Different types of government and governance in the blockchain

This research work, a study was carried out on blockchain technology and its types, as well as the creation of new models of government and governance from the scope of an organization, infrastructure and platform. Governance and commercial models were addressed, based on standardization of data and legal frameworks. On the other hand, it showed how operational governance causes consequences in business models, whether with transactions, multi-signature, forks, consensus mechanism, smart contracts, tokenization, online dispute resolution and decentralized application (World Economic Forum, 2020, pp. 97 196). It was discovered that at least in current business models, private blockchain networks are more useful than public networks because they have greater operational flexibility and data governance, without exempting that public networks must also have mechanisms of governance since sometimes a human consensus must be reached to make updates to protocols and technical rules (The Law Society, 2020, pp. 24-61). This paper shows the basic principles that must be observed about governance and regulation in the implementation of blockchain technologies in systems created by governments, corporations and/or organized civil societies.
The inductive bias of ReLU networks on orthogonally separable data

We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of two max-margin classifiers: one corresponding to the positive data subset and one corresponding to the negative data subset. The proof is based on the recently introduced concept of extremal sectors, for which we prove a number of properties in the context of orthogonal separability. In particular, we prove stationarity of activation patterns from some time T onwards, which enables a reduction of the ReLU network to an ensemble of linear subnetworks.
Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks

Recurrent cortical networks provide reservoirs of states that are thought to play a crucial role for sequential information processing in the brain. However, classical reservoir computing requires manual adjustments of global network parameters, particularly of the spectral radius of the recurrent synaptic weight matrix. It is hence not clear if the spectral radius is accessible to biological neural networks. Using random matrix theory, we show that the spectral radius is related to local properties of the neuronal dynamics whenever the overall dynamical state is only weakly correlated. This result allows us to introduce two local homeostatic synaptic scaling mechanisms, termed flow control and variance control, that implicitly drive the spectral radius towards the desired value under working conditions. We demonstrate the effectiveness of the two adaptation mechanisms under different external input protocols and the network performance after adaptation by training the network to perform a time-delayed XOR operation on binary sequences. As our main result, we found that flow control reliably regulates the spectral radius for different types of input statistics. Precise tuning is however negatively affected when interneural correlations are substantial. Furthermore, we found a consistent task performance over a wide range of input strengths/variances. Variance control did however not yield the desired spectral radii with the same precision, being less consistent across different input strengths. Given the effectiveness and remarkably simple mathematical form of flow control, we conclude that self-consistent local control of the spectral radius via an implicit adaptation scheme is an interesting and biological plausible alternative to conventional methods using setpoint homeostatic feedback controls of neural firing.
Interpreting Knowledge Graph Relation Representation from Word Embeddings

Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-specific mapping. Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained. Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations. We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justified by our analysis.
How to Handle Health-Related Small Imbalanced Data in Machine Learning?

Abstract When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology , Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.
Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection.In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service.Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree.Secondly, we establish a new trust evaluation and give the corresponding weight algorithm.Thirdly, we quantify the privacy information with the normal space and correlation coefficient method.Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference.Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method.Finally, we summarize the work of this paper and point out the future development direction.
Image compression based on Gaussian mixture model constrained using Markov random field

We introduce a Gaussian Mixture Model (GMM) constrained by Markov Random Field (MRF) framework for image compression in this paper. The image is predicted using GMM with MRF and the parameters of the GMM are estimated using an adjusted Expectation-Maximization (EM) algorithm. Mixture Model Optimization (MMO) is used in this framework to select the optimal number of distributions and avoid local optimum of EM at the same time. Parameters are encoded using fixed-length bits. A codebook is used to improve the coding efficiency of the covariance parameters. The residual between the original image and the prediction is encoded using High Efficiency Video Coding (HEVC) intra coding. Experimental results show that our method performs better than our previous work, HEVC, JPEG 2000 and Better Portable Graphics (BPG) which is an improved version of HEVC.
Transfer-tensor description of memory effects in open-system dynamics and multi-time statistics

Abstract The non-Markovianity of an arbitrary open quantum system is analyzed in reference to the multi-time statistics given by its monitoring at discrete times. On the one hand, we exploit the hierarchy of inhomogeneous transfer tensors (TTs), which provides us with relevant information about the role of correlations between the system and the environment in the dynamics. The connection between the TT hierarchy and the CP-divisibility property is then investigated, by showing to what extent quantum Markovianity can be linked to a description of the open-system dynamics by means of the composition of one-step TTs only. On the other hand, we introduce the set of stochastic TT transformations associated with local measurements on the open system at different times and conditioned on the measurement outcomes. The use of the TT formalism accounts for different kinds of memory effects in the multi-time statistics and allows us to compare them on a similar footing with the memory effects present in non-monitored non-Markovian dynamics, as we illustrate on a spin-boson case study.
Bilateral attention network for semantic segmentation

Enhancing network feature representation capabilities and reducing the loss of image details have become the focus of semantic segmentation task. This work proposes the bilateral attention network for semantic segmentation. The authors embed two attention modules in the encoder and decoder structures . Specifically, high-level features of the encoder structure integrate all channel maps through dense channel relationships learned by the channel correlation coefficient attention module. The positively correlated channels promote each other, and the negatively correlated channels suppress each other. In the decoder structure, low-level features selectively emphasize the edge detail information in the feature map through the position attention module. The feature expression of semantic segmentation is improved by feature fusion of the two attention modules to obtain more accurate segmentation results . Finally, to verify the effectiveness of the model, the authors conduct experiments on the PASCAL VOC 2012 and Cityscapes scene analysis benchmark data sets and achieve a mean intersection-over-union of 74.92% and 66.63%, respectively.
Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks

Graph representation learning has made major strides over the past decade.However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions.In such cases, the relationships in the data are better represented as hyperedges (set of entities) of a non-uniform hypergraph.While there have been works on principled methods for learning representations of nodes of a hypergraph, these approaches are limited in their applicability to tasks on non-uniform hypergraphs (hyperedges with different cardinalities).In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices.Specifically, for a given vertex set, we propose frameworks for (1) hyperedge classification and (2) variable sized expansion of partially observed hyperedges which captures the higher order interactions among vertices and hyperedges.We evaluate performance on multiple real-world hypergraph datasets and demonstrate consistent, significant improvement in accuracy, over state-of-the-art models.
Analytics in the publication of a scientific journal

It is shown that the analysis of editorial and publishing processes helps to estimate the time of passing manuscripts in the editorial office of a scientific journal and to control the contractor when performing technical work on literary editing and layout of articles in the journal. For analytics, the apparatus of probability theory and mathematical statistics is used, as well as time series for predicting the dynamics of incoming articles and predicting the prospects for their publication. Based on the results obtained, it is shown that the study of time indicators of the processes of arrival and acceptance of articles for publication, reviewing, literary editing, layout and distribution and receipt of license agreements allows you to plan the work on the filling of articles in journal issues for the year ahead, monitor the work of reviewers on the examination of articles sent to them and manage the contractor preparing issues for publication, when performing technical work. All the considered analytical methods are available for use by the editors of the scientific journal. To simplify the compilation and work with the proposed analytics tools, we recommend software for working with spreadsheets.
Anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network

The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser-based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor-based sensor or tapered element oscillating microbalance-based sensor. However, an LLS-based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP-GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS-based PM measuring sensors. We conclude that our HP-GAN is a cutting-edge model for anomaly detection.