Reviewerly/exHarmony huggingface dataset:
Latte: Cross-framework Python package for evaluation of latent-based generative models

Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation. Latte is compatible with both PyTorch and TensorFlow/Keras, and provides both functional and modular APIs that can be easily extended to support other deep learning frameworks. Using NumPy-based and framework-agnostic implementation, Latte ensures reproducible, consistent, and deterministic metric calculations regardless of the deep learning framework of choice.
Universities as Subjects of Scientific Law

The article analyzes the legal problems of the integration of science and education, offers an assessment of various organizational forms of integration of science and higher education over the past 30 years. The main provisions, stages of implementation and the results of the implementation of various scientific and educational state programs are studied, statistical data are provided that emphasize the increase in the volume of scientific activities carried out in educational organizations (higher educational institutions). The authors investigate the status of educational organizations as subjects of scientific law, emphasize the imperfection of Russian educational legislation regarding the differentiation of educational institutions of higher education and scientific organizations. The article focuses on the study of the positive and negative consequences of the introduction of a new category of higher educational institutions – flagship universities. After analyzing the problems of legal regulation of flagship universities, the authors come to the conclusion that the logical conclusion of the reform of flagship universities can be the establishment of the status of flagship universities in the legislation on education and a clear separation of flagship and federal universities.
Development of echinoderm comic as learning media in Junior High School

Comic media development is essential for junior high school students. Because in learning the echinoderm material, students' motivation is shallow. This research aims to 1) develop echinoderms comics, 2) find out how to develop comic learning media, 3) find out whether comic learning media was suitable for scientific learning. This research and development used the Hannafin and Peck model with three stages they were 1) needs analysis, 2) design stage and 3) development and implementation. The methods of collecting data were used observation and questionnaires. Comic media validation consisted of material experts, media experts, and educators. The subjects in this research were students of class VII Junior High School Islam Integral Luqman Al-Hakim 02 Batam. The quality of comic media is based on material expert validation, a suitable category (85.14%). The result of media expert validation was an excellent category (92.00%). Students who responded at the small group test stage were an excellent category (93.61%). Finally, students answered a suitable category at the big group test (88.25%). Echinoderm comics have been ideal for science learning based on the explanation above.
On the Taylor expansion of $\lambda$-terms and the groupoid structure of their rigid approximants

We show that the normal form of the Taylor expansion of a $\lambda$-term is isomorphic to its B\"ohm tree, improving Ehrhard and Regnier's original proof along three independent directions. First, we simplify the final step of the proof by following the left reduction strategy directly in the resource calculus, avoiding to introduce an abstract machine ad hoc. We also introduce a groupoid of permutations of copies of arguments in a rigid variant of the resource calculus, and relate the coefficients of Taylor expansion with this structure, while Ehrhard and Regnier worked with groups of permutations of occurrences of variables. Finally, we extend all the results to a nondeterministic setting: by contrast with previous attempts, we show that the uniformity property that was crucial in Ehrhard and Regnier's approach can be preserved in this setting.
Ensuring trustworthy and ethical behaviour in intelligent logical agents

Abstract Autonomous intelligent agents are employed in many applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. A priori certification techniques (i.e. techniques applied prior to system’s deployment) can be useful, but are not sufficient for agents that evolve, and thus modify their epistemic and belief state, and for open multi-agent systems, where heterogeneous agents can join or leave the system at any stage of its operation. In this paper, we propose/refine/extend dynamic (runtime) logic-based self-checking techniques, devised in order to be able to ensure agents’ trustworthy and ethical behaviour.
One Versus all for deep Neural Network for uncertaInty (OVNNI) quantification

Deep neural networks (DNNs) are powerful learning models yet their results are not always reliable. This drawback results from the fact that modern DNNs are usually overconfident, and consequently their epistemic uncertainty cannot be straightforwardly characterized. In this work, we propose a new technique to quantify the epistemic uncertainty of data easily. This method consists in mixing the predictions of an ensemble of DNNs trained to classify One class vs. All the other classes (OVA) with predictions from a standard DNN trained to perform All vs. All (AVA) classification. First of all, the adjustment provided by the AVA DNN to the score of the base classifiers allows for a more fine-grained inter-class separation. Moreover, the two types of classifiers enforce mutually their detection of out-of-distribution (OOD) samples, circumventing entirely the requirement of using such samples during training. The additional cost involved by the construction of the ensemble is offset by the ease of use of our proposed strategy and by its enhanced generalization potential, as it does not bind its performance in a given context to specific OOD datasets. The extensive experiments confirm the wide applicability of our approach, and our method achieves state of the art performance in quantifying OOD data across multiple datasets and architectures while requiring little hyper-parameter tuning.
Improved unitary uncertainty relations

We derive strong variance-based uncertainty relations for arbitrary two and more unitary operators by re-examining the mathematical foundation of the uncertainty relation. This is achieved by strengthening the celebrated Cauchy–Schwarz inequality using a method of brackets and convex functions. The unitary uncertainty relations outperform several strong unitary uncertainty relations, notably better than some recent best lower bounds such as [Phys. Rev. Lett. 120, 230402 (2018)] and [Phys. Rev. A. 100, 022116 (2019)].
Blockchain Abnormal Transaction Behavior Analysis: a Survey

Blockchain technology has been known to the public since cryptocurrencies such as Bitcoin were introduced. While blockchain technology is being developed and used by researchers, the technology is also used to conduct abnormal transaction behaviors, such as money laundering and fraud. After understanding a large amount of research data, this paper summarizes and analyzes the abnormal trading behaviors in blockchains from the basic characteristics of smart contracts and the topology of blockchain networks. These works provide a reference direction for future researchers.
Providing Visibility to Recording of Related Transactions Using Blockchain Technology by Shifting from Double Entry to Triple Entry

Blockchain is a decentralized digital ledger on which transactions can be recorded chronologically that cannot be altered or destroyed and can be viewed by all the participants in the network. This technology seeks to improve the accounting functions by lowering the overheads of maintaining and reconciling ledgers and offering complete transparency into asset ownership and history. This article provides an insight about the concept of blockchain technology, its framework and application in different fields. It focuses on the principle of triple entry system used in blockchain accounting and explores the prospects and challenges of shifting to blockchain accounting.
A Vertical Federation Framework Based on Representation Learning

Vertical federated learning aims to use different attribute sets of the same user to jointly build a model without disclosing its original data or model parameters. Facing the challenges brought by massive data in the Internet of Things, based on representation learning, advanced features are accurately and efficiently extracted to prepare for downstream tasks. At the same time, when implementing federated learning in the Internet of Things environment, it is necessary to consider the computing power of a large number of edge devices. In real life, more non-Euclidean data is used. For non-Euclidean data, we use graph neural networks as encoders to obtain better high-level feature vectors with fewer parameters. We propose a new vertical federated learning framework based on representation learning, combined with graph neural networks, to build a better model in the massive non-Euclidean data of the Internet of Things, while protecting the data privacy of each data owner.
Career and Professional Development of the Head of the Organization

The present research featured the career and professional development of the head of a modern organization. The status and career development of specialists and managers often come into dissonance. Managerialism, corruption, and other deformations create contradictions that require targeted and large-scale efforts, e.g. innovative career management technologies. The research objective was to analyze modern problems of managing the career and professional development of CEO personnel. The career and professional path of a CEO is a cultural phenomenon. They are closely related to one's development as a personality, a partner, and a specialist, which forms a system of competencies. These competencies also reflect and generate changes in the area of intrapersonal and interpersonal improvement. Personal development depends on the accumulation and processing of life and career experience. As a rule, this experience is neither purposeful nor reflexive. As a result, it gives little large-scale effect. The study and reflection should prevent and correct violations of professional-labor and social-career development, including careerograms, customized work trajectories, psychotechnologies of career management, etc. Performance of a manager is a multilevel system of interconnected individual components. Managerial performance can be improved as a systemic correlation of the existing and emerging qualities of a manager with the peculiarities of the organization, i.e. corporate culture, socio-psychological climate, internal education and mutual assistance, a system of selection and support, a system of professionally important qualities of an effective leader, ergo design of staff, psychophysiological harmony, etc.
Perceiving university students' opinions from Google app reviews

Abstract Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The critique's viewpoint regarding an app is proportional to their satisfaction level. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university students' behavior toward the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF‐IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, GloVe, on the deep learning paradigms. Our model was trained on Google app reviews and tested on students' app reviews (SAR). The various combinations of these algorithms were compared among each other using F‐score and accuracy and inferences were highlighted graphically. SVM, among other classifiers, gave fruitful accuracy (93.41%), F‐score (0.89) on bi‐gram + TF‐IDF scheme. Bagging enhanced the performance of LR and NB with accuracy 87.88% and 86.69% and F‐score 0.86 and 0.78 respectively. Overall, LSTM on Glove embedding recorded the highest accuracy (95.2%) and F‐score (0.88).
Marketing strategies: innovation in retail during the restriction and social distance measures caused by the Covid-19 pandemic / Estratégias de marketing: inovação no varejo durante as medidas de restrição e distância social causadas pela pandemia de Covid-19

This study aims to understand the changes and perceptions of retailers related to marketing strategies in innovation in the empirical reality of retail, during the measures of restriction and social and commercial distancing caused by the COVID-19 pandemic. This is a qualitative content analysis study, carried out through interviews. The objects of study of the investigation were companies operating in retail in the city of Criciúma (SC). The results indicate that the main change in retail companies was the growing use of digital media, including websites, Instagram, Facebook and WhatsApp. The lack of understanding of the theory of innovation in marketing was present in the testimonies of the interviewees. Thus, it is observed that innovation in marketing is something new and still needs to be explored in economic sectors. The testimonies showed that the innovation stems from the marketing need and responsibility of store owners for monitoring the progress of activities, with no sharing of ideas and decisions between the different areas to facilitate decision-making. The and studo contributed to expand the understanding of the marketing strategies of innovation in empirical reality of retailers and how this impacted directly on their business.
Robustly decorrelating errors with mixed quantum gates

Abstract Coherent errors in quantum operations are ubiquitous. Whether arising from spurious environmental couplings or errors in control fields, such errors can accumulate rapidly and degrade the performance of a quantum circuit significantly more than an average gate fidelity may indicate. As Hastings (2017 Quantum Inf. Comput. 17 488) and Campbell (2017 Phys. Rev. A 95 042306) have recently shown, by replacing the deterministic implementation of a quantum gate with a randomized ensemble of implementations, one can dramatically suppress coherent errors. Our work begins by reformulating the results of Hastings and Campbell as a quantum optimal control problem. We then discuss a family of convex programs able to solve this problem, as well as a set of secondary objectives designed to improve the performance, implementability, and robustness of the resulting mixed quantum gates. Finally, we implement these mixed quantum gates on a superconducting qubit and discuss randomized benchmarking results consistent with a marked reduction in the coherent error.
Adversarial Defenses via a Mixture of Generators

In spite of the enormous success of neural networks, adversarial examples remain a relatively weakly understood feature of deep learning systems. There is a considerable effort in both building more powerful adversarial attacks and designing methods to counter the effects of adversarial examples. We propose a method to transform the adversarial input data through a mixture of generators in order to recover the correct class obfuscated by the adversarial attack. A canonical set of images is used to generate adversarial examples through potentially multiple attacks. Such transformed images are processed by a set of generators, which are trained adversarially as a whole to compete in inverting the initial transformations. To our knowledge, this is the first use of a mixture-based adversarially trained system as a defense mechanism. We show that it is possible to train such a system without supervision, simultaneously on multiple adversarial attacks. Our system is able to recover class information for previously-unseen examples with neither attack nor data labels on the MNIST dataset. The results demonstrate that this multi-attack approach is competitive with adversarial defenses tested in single-attack settings.
Efficient analytical computation of expected frequency of motifs of small size by marginalization in uncertain network

Counting motifs in an uncertain graph for which each link is associated with a connection probability is computationally expensive when the graph is huge due to the extremely large number of possible worlds. Natural approach is to rely on sampling-based approximation methods, but this still needs many sample graphs for obtaining accurate results. We propose a novel method that analytically computes the expected frequency of motif without relying on expensive sampling. Marginalizing the probability of each possible world on a candidate motif can drastically reduce the number of possible worlds that need be considered when the size of motif is small. Experiments using real-world data confirm that the proposed method is effective and efficient. It is far better than the state-of-the-art sampling-based method. The accuracy is guaranteed and the running time is about 4 order of magnitude faster. It runs at a speed that does not depend on the connection probability.
Optimal Analysis of Human Resources Allocation in Colleges and Universities Based on Internet of Things Technology

This article takes the development and superiority of the human resource system under the IoT technology as the background and adopts the popular IoT architecture and the current latest technical theories to develop and improve the human resource management system. First, we introduce the establishment of the J2EE development environment and then introduce the realization of the three layers of the overall IoT architecture of the system, namely, the realization of the presentation layer, the realization of the layer, and the realization of the data and then the realization of the main modules of the system; finally, we explain the function test of some modules of the system. After that, we mainly analyze the requirements of the system, first the overall requirements analysis and then the function and nonfunctional requirements analysis of the system. On the basis of perfecting the service-oriented modeling of manufacturing resources, the integration and optimization of resources are carried out according to the requirements of manufacturing tasks, and the discovery mechanism and matching methods of manufacturing resource services are studied to reduce the solution space of resource allocation. Aiming at the problem of optimal utilization of manufacturing resources, this paper establishes a scientific and reasonable evaluation index system to reduce resource service costs and improve resource utilization. At the same time, after analyzing the problem of real-time distribution of resources in the manufacturing Internet of Things environment, the real-time information of resource distribution is designed. The interactive mechanism and a two-tier optimization method based on real-time information-driven resource distribution tasks are proposed. The simulation experiment results show that the optimization method proposed in this paper, compared with the traditional resource distribution method, not only reduces the carrying distance of human resource distribution but also reduces the empty load rate, thereby reducing the waste of human resources.
Design and Implementation of Intelligent Speech Recognition System Based on FPGA

Abstract Under certain circumstances, where GPU is not available, the computing power of the CPU alone may not be sufficient enough to run speech recognition system efficiently. As a reconfigurable heterogeneous device, FPGA has the characteristics of high parallelism, low power consumption, and high flexibility. It can support high-performance computing acceleration for speech recognition applications. In order to implement speech recognition system on an specified computing platform, a speech recognition system architecture is proposed, which is based on software and hardware cooperative computing technology that focus on FPGA. First, a system architecture based on gated convolutional neural network is designed, with the simplification of the traditional speech recognition system architecture. Then a modular design idea is used to map the gated convolutional neural network operator to the FPGA hardware logic. Further through the state machine to schedule and control the data stream and operators, to achieve the flexible acceleration of the voice recognition network model. AISHELL data set is used to carry out the test, where presents remarkable improvement on computing performance, compared with Feiteng 1500A processor, the efficiency is improved by 18.1 times.
Driving Active Contours to Concave Regions

Broken characters restoration represents the major challenge of optical character recognition (OCR). Active contours, which have been used successfully to restore ancient documents with high degradations have drawback in restoring characters with deep concavity boundaries. Deep concavity problem represents the main obstacle, which has prevented Gradient Vector Flow active contour in converge to objects with complex concavity boundaries. In this paper, we proposed a technique to enhance (GVF) active contour using particle swarm optimization (PSO) through directing snake points (snaxels) toward correct positions into deep concavity boundaries of broken characters by comparing with genetic algorithms as an optimization method. Our experimental results showed that particle swarm optimization outperform on genetic algorithm to correct capturing the converged areas and save spent time in optimization process.
Neural Character-Level Syntactic Parsing for Chinese

In this work, we explore character-level neural syntactic parsing for Chinese with two typical syntactic formalisms: the constituent formalism and a dependency formalism based on a newly released character-level dependency treebank. Prior works in Chinese parsing have struggled with whether to de ne words when modeling character interactions. We choose to integrate full character-level syntactic dependency relationships using neural representations from character embeddings and richer linguistic syntactic information from human-annotated character-level Parts-Of-Speech and dependency labels. This has the potential to better understand the deeper structure of Chinese sentences and provides a better structural formalism for avoiding unnecessary structural ambiguities. Specifically, we first compare two different character-level syntax annotation styles: constituency and dependency. Then, we discuss two key problems for character-level parsing: (1) how to combine constituent and dependency syntactic structure in full character-level trees and (2) how to convert from character-level to word-level for both constituent and dependency trees. In addition, we also explore several other key parsing aspects, including di erent character-level dependency annotations and joint learning of Parts-Of-Speech and syntactic parsing. Finally, we evaluate our models on the Chinese Penn Treebank (CTB) and our published Shanghai Jiao Tong University Chinese Character Dependency Treebank (SCDT). The results show the e effectiveness of our model on both constituent and dependency parsing. We further provide empirical analysis and suggest several directions for future study.