Reviewerly/exHarmony huggingface dataset:
Trends of pedagogical education – main directions of development of Pedagogical Institute

We consider the main trends in the development of pedagogical education, among which are highlighted: the formation of a system of continuing pedagogical education; development of digital pedagogy: e-learning and e-learning environment; reliance on practical orientation in the educational process and personalization of education; development of STEAM-education and the formation of educational ecosystem, taking into account the principles of openness, flex-ibility, scalability. Based on the proposed trends, we describe the main guidelines and develop-ment directions of the Pedagogical Institute of the Federal State Budgetary Educational Institution of Higher Education “Derzhavin Tambov State University”. The experience of implementing the regional project “New models of pedagogical classes” is described: the models “Pedagogical pre-university” and “Network pedagogical class”; organization and implementation of the online learning process in a pandemic; experience in the implementation of pedagogical and socially significant projects and activities that form the so-called educational ecosystem at the Pedagogical Institute, etc. Among the main development prospects are: specification and expansion of the nomenclature of programs of pedagogical areas of training with two profiles; development and implementation of pedagogical internship programs; creation and approbation of didactic simulators as part of the development of the electronic educational environment of the Pedagogical Institute, etc.
MoBoAligner: A Neural Alignment Model for Non-Autoregressive TTS with Monotonic Boundary Search

To speed up the inference of neural speech synthesis, non-autoregressive models receive increasing attention recently. In non-autoregressive models, additional durations of text tokens are required to make a hard alignment between the encoder and the decoder. The duration-based alignment plays a crucial role since it controls the correspondence between text tokens and spectrum frames and determines the rhythm and speed of synthesized audio. To get better duration-based alignment and improve the quality of non-autoregressive speech synthesis, in this paper, we propose a novel neural alignment model named MoboAligner. Given the pairs of the text and mel spectrum, MoboAligner tries to identify the boundaries of text tokens in the given mel spectrum frames based on the token-frame similarity in the neural semantic space with an end-to-end framework. With these boundaries, durations can be extracted and used in the training of non-autoregressive TTS models. Compared with the duration extracted by TransformerTTS, MoboAligner brings improvement for the non-autoregressive TTS model on MOS (3.74 comparing to FastSpeech's 3.44). Besides, MoboAligner is task-specified and lightweight, which reduces the parameter number by 45% and the training time consuming by 30%.
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model

As neural network classifiers are deployed in real-world applications, it is crucial that their failures can be detected reliably. One practical solution is to assign confidence scores to each prediction, then use these scores to filter out possible misclassifications. However, existing confidence metrics are not yet sufficiently reliable for this role. This paper presents a new framework that produces a quantitative metric for detecting misclassification errors. This framework, RED, builds an error detector on top of the base classifier and estimates uncertainty of the detection scores using Gaussian Processes. Experimental comparisons with other error detection methods on 125 UCI datasets demonstrate that this approach is effective. Further implementations on two probabilistic base classifiers and two large deep learning architecture in vision tasks further confirm that the method is robust and scalable. Third, an empirical analysis of RED with out-of-distribution and adversarial samples shows that the method can be used not only to detect errors but also to understand where they come from. RED can thereby be used to improve trustworthiness of neural network classifiers more broadly in the future.
SELF-CONTROL METHOD IN FORMING A PROFESSIONALLY COMPETENT PERSON DURING THE LEARNING PROCESS AT THE UNIVERSITY

The issue of personal potential mobilization to solve the problems in future professional activities of the University graduates with self-control method is discussed. The implementation of the competence approach in the higher education system is associated with the search and use of methods and techniques for effective independent study. The purpose of our research is to determine the place of self – control method in the learning process. The study and questionnaire revealed a low level of self-control development and its rare use in the learning process.
RETRACTED: Analysis of the Status Quo of Employment and Entrepreneurship of College Students in the New Era Based on Big Data Analysis and Discussion

Abstract With the continuous development of China’s social economy, the social demand for talent is increasing but due to the fierce competition between the markets, it also brings many problems for many college students’ employment and entrepreneurship. Employment and entrepreneurship of college students have become serious. Based on this, this article first analyzes the current situation of college students’ employment and entrepreneurship in the new era based on big data analysis, and then conducts an in-depth discussion on the development path of college students in the new era of employment and entrepreneurship.
WEBSITE SEBAGAI SARANA PROMOSI UKM SOPYAN

Sopyan UKM is one of Palembang's craftsmen of Palembang woven fabric cloths / tajung located in the Tuan Kentang area of Palembang which is led by Mr. Sopyan Candra. The internet is a tool used by UKM Sopyan in introducing and promoting its products. Both through the website and social media such as Instagram, line, and WhatsApp. For this reason, counseling needs to be given on how to create a Website and introduce equipment used in the process of making woven woven cloths / tajung to Sopyan Palembang UKM.
Segment Aggregation for Short Utterances Speaker Verification Using Raw Waveforms

Most studies on speaker verification systems focus on long-duration utterances, which are composed of sufficient phonetic information. However, the performances of these systems are known to degrade when short-duration utterances are inputted due to the lack of phonetic information as compared to the long utterances. In this paper, we propose a method that compensates for the performance degradation of speaker verification for short utterances, referred to as aggregation. The proposed method adopts an ensemble-based design to improve the stability and accuracy of speaker verification systems. The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding. Then, this method simultaneously trains the segment embeddings and the aggregated speaker embedding. In addition, we also modified the teacher-student learning method for the proposed method. Experimental results on different input duration using the VoxCeleb1 test set demonstrate that the proposed technique improves speaker verification performance by about 45.37% relatively compared to the baseline system with 1-second test utterance condition.
Design Thinking and Knowledge Engineering: A Machine Learning Case

Knowledge and design captured in the heads of people is a starting point for the abductive design process in which design thinking and its tools support the ideation and externalization of design prescriptions.Turning these design prescriptions into innovative solutions implies to further decompose design thinking artifacts while enriching them with semantics, as this enables a bridging opportunity to conceptual models representing actual enterprise and operational contexts.Realizing the bridging opportunity requires to intertwine digital twins of design thinking artifacts with the semantic expressiveness of conceptual modelling methods.The problem is that this requires a considerable knowledge engineering effort.To ease the problem, machine learning concepts are introduced into the Design2Model approach employed in OMiLAB.Furthermore, a corresponding tool is realized.As a result, the paper at hand reports on a novel application case in which machine learning and knowledge engineering is combined to realizein an effective waythe bridging opportunity between design thinking and conceptual modelling.In particular, the case covers a component of the Design2Model tool responsible for digitalizing, semantifying, and processing of Post-it Notes from a design thinking workshop and their representations in diagrammatic conceptual models.The report focuses on technologies used and their qualitative artificial ex-post evaluation in a laboratory environment.
Following the Pebble Trail

It is widely known that return-oriented programming (ROP) attack can be mounted on x86, ARM and SPARC architectures. However, it remained an open question if ROP was possible on RISC-V, a new and promising free and open instruction set architecture (ISA). In this paper we present a novel ROP technique specific to RISC-V architecture. Our method relies on the processor's saved registers and its function calling convention. We use functional gadgets (that perform primitive operations) ended in a jump instruction to an address held in a saved register. The order of gadgets chaining is given by a novel gadget, which we call the charger gadget, which loads the saved registers with the gadgets? addresses from the stack. We constructed a library of gadgets extracted from the standard Linux libraries. Finally, we evaluated our method by exploiting a buffer-overflow vulnerable application.
Reading Between the Text(s) - Interpreting Emoji and Emoticons in the Australian Criminal Law Context

Emoji and emoticons are becoming increasingly prevalent in criminal law cases around the world, although some judicial officers currently have no experience of them or of interpreting them. This article offers an in-depth examination of the area of emoji and emoticons in criminal law. It provides an overview of emoticons and emoji, including their history, their global popularity of use in modern communication, and their particular interpretative difficulties. It canvasses and analyzes international criminal cases involving emoji and emoticons. It provides a valuable table that lists each such case in which emoji and emoticons are present. In reviewing the existing international case law, this article identifies the handful of cases in which judicial officers have attempted to interpret emoji and emoticons in the criminal law context. However, it also demonstrates the absence of clear guidance on methods of interpreting emoji and emoticons. Most importantly, this article aims to assist filling this void by proffering important recommendations regarding interpreting emoji and emoticons in the criminal law context.
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems. Missing-at-random (MAR) data, namely randomized controlled trials (RCTs), are usually required by most previous counterfactual learning methods for debiasing learning. However, the execution of RCTs is extraordinarily expensive in practice. To circumvent the use of RCTs, we build an information-theoretic counterfactual variational information bottleneck (CVIB), as an alternative for debiasing learning without RCTs. By separating the task-aware mutual information term in the original information bottleneck Lagrangian into factual and counterfactual parts, we derive a contrastive information loss and an additional output confidence penalty, which facilitates balanced learning between the factual and counterfactual domains. Empirical evaluation on real-world datasets shows that our CVIB significantly enhances both shallow and deep models, which sheds light on counterfactual learning in recommendation that goes beyond RCTs.
Graph Regularized Variational Ladder Networks for Semi-Supervised Learning

To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi-supervised learning. However, It ignores the manifold information of high-dimensional data and usually achieves unmeaning features which are very difficult to use in the subsequent tasks, such as prediction and recognition. To these issues, we proposed Graph Regularized Variational Ladder Networks (GRVLN), which explicitly and implicitly employs the manifold structure of data. Our contributions can be summarized as two folds: (1) Graph regularization is used to build all decoder layers, which explicitly promotes the manifold learning via graph laplacian matrixs; (2) Variational autoencoder is used as the backbone instead of traditional autoencoder in the encoder layers for implicitly learning the manifold structure of data distribution. Compared with ladder networks and other autoencoder-based methods, GRVLN achieves superior performance in semi-supervised classification tasks. Experimental results show that our method also has a comparable performance with state-of-the-art methods on several benchmark data sets.
A New Neural Search and Insights Platform for Navigating and Organizing AI Research

To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
Journals’ hidden self-citation as the problem of publication ethics

Twenty two science Russian periodicals in informatics and library studies are selected for the bibliometrical analysis of key journal indicators, including publication activity of the same journals’ editorial staff. For the first time for domestic journals, the study reveals hidden self-citation when editorial members include links to their journal from other publications. The available instruments of scientometrical databases, including Web of Science and Scopus, and the national system Russian Science Citation Index do not enable to identify this form of self-citation. The mentioned manipulations are aimed at boosting journal rating. In several cases, intensive and unjustified citation by journals’ editorial staff in other periodicals which we consider the violation of publication ethical principles, is revealed.
Continuous dynamical decoupling and decoherence-free subspaces for qubits with tunable interaction

Protecting quantum states from the decohering effects of the environment is of great importance for the development of quantum computation devices and quantum simulators. Here, we introduce a continuous dynamical decoupling protocol that enables us to protect the entangling gate operation between two qubits from the environmental noise. We present a simple model that involves two qubits which interact with each other with a strength that depends on their mutual distance and generates the entanglement among them, as well as in contact with an environment. The nature of the environment, that is, whether it acts as an individual or common bath to the qubits, is also controlled by the effective distance of qubits. Our results indicate that the introduced continuous dynamical decoupling scheme works well in protecting the entangling operation. Furthermore, under certain circumstances, the dynamics of the qubits naturally led them into a decoherence-free subspace which can be used complimentary to the continuous dynamical decoupling.
OAUTH 2.0 IN SECURING APIS

Today’s API are used for a variety of things such as passing data to another webservice, reading data from a database etc. The problem with this is that not all the API`s are secure. Most of the todays API are old and rely only on an authentication token or string which is often hardcoded. We will focus on OAUTH 2.0 as a new protocol in securing our API`s. This is a new protocol based on delegation of authorization, dynamically changing authentication string based on user session or application session. We will go on this different modes of authentication and show you how to use them properly. We will set up this with a Web Api integrated with OAUTH and a client application that will stimulate the requests to our API.
Measuring nonclassicality of single-mode systems

A beam splitter can generate entanglement if the input field is nonclassical, e.g. squeezed. Thus, beam splitter transformation can be utilized for obtaining single-mode nonclassicality conditions from two-mode entanglement criteria. Here, we demonstrate that (i) the generalized quadrature-squeezing condition $\langle\hat{a}^\dagger\hat{a}\rangle < |\langle \hat{a}^2\rangle|$ can be obtained from the two-mode entanglement criterion of Duan-Giedke-Cirac-Zoller~[PRL 84, 2722]. (ii) Single-mode nonclassicality condition, obtained from the two-mode entanglement criterion of Simon~[PRL, 84 2726], can detect (also accompanies) the nonclassicality of a superradiant ground state where no quadrature-squeezing takes place. (iii) We present an explicit form for a single-mode nonclassicality measure, for Gaussian states, via maximization of the beam splitter output with respect to the beam splitter parameters. We measure the nonclassicality of a squeezed state and the one generated by a damped second harmonic crystal cavity. We determine pump strengths where single-mode nonclassicality onsets, i.e. resists against the vacuum noise.
Trust, transparency and disintermediation: an analysis of blockchain implementation in the supply-chain

Blockchain represents the backbone of cryptocurrencies, their underlying technology. It can be seen as a large database shared by a peer-to-peer network of users, which collectively validates the information that is recorded on it. Not residing on a single central server, it overcomes a centralised logic, the one used by the best-known web applications like Amazon, Google or Facebook. Instead, DApps (DApp being the acronym of Decentralised Application) are distributed, decentralised applications specifically designed to run on blockchain systems. Blockchain does not involve intermediaries or trusted third parties and its main feature lies in its ability to increase transparency and traceability in transactions. A recent resolution of the European Parliament called for measures to increase the adoption of public and private blockchains, particularly in supply chain management, stating that they can have an indirect effect both at a social and economic level. Several studies have already analysed the potential of blockchain in the agri-food sector, but also in the production and distribution chain of luxury goods or other consumer goods. The existing literature focuses mainly on the role of blockchain in improving the processes related to safety and efficiency of the supply chain, yet it does not seem to problematize the concept of transparency. Despite this, the relationship between transparency and trust, which is generally confirmed by literature, is far from linear. Following an introduction of the technical characteristics of blockchain technology and its possible implementation, we address the way in which blockchain affects the issues of disintermediation, such as in the public sector, where the role of blockchain is still being defined. We also focus on the way blockchain shifts the centre of gravity of trust from an institutional, central entity, either towards the periphery or towards a technological system. Comparing the constructs of trust and transparency, we question the a priori desirability of transparency. In fact, the relationship between trust and transparency becomes rather complex in organizational communication processes. A spotlight is also placed on the aspects that affect the relationship between an organization implementing the blockchain and its relevant stakeholders, along with their different needs. We argue that blockchain still leaves a need for a relationship of trust, as much as the achievement of a trust-free model is pursued. Finally, we mention some of the major critical issues of blockchain, which are still to be resolved.
Merging existential rules programs in multi-agent contexts through credibility accrual

Merging operators represent a significant tool to extract a consistent and informative view from a set of agents. The consideration of practical scenarios where some agents can be more credible than others has contributed to substantially increase the interest in developing systems working with trust models. In this context, we propose an approach to the problem of merging knowledge in a multiagent scenario where every agent assigns to other agents a value reflecting its perception on how credible each agent is. The focus of this paper is the introduction of an operator for merging Datalog± ontologies considering agents’ credibility. We present a procedure to enhance a conflict resolution strategy by exploiting the credibility attached to a set of formulas; the approach is based on accrual functions that calculate the value of formulas according to the credibility of the agents that inform them. We show how our new operator can obtain the best-valued knowledge base among consistent bases available, according to the credibilities attached to the sources.
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings

The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints.This poses a challenge to their deployment for voice assistants such as Amazon Alexa and Google Assistant on edge devices with limited memory budgets.We propose to learn compositional code embeddings to greatly reduce the sizes of BERT-base and RoBERTa-base.We also apply the technique to DistilBERT, ALBERT-base, and ALBERT-large, three already compressed BERT variants which attain similar state-of-the-art performances on semantic parsing with much smaller model sizes.We observe 95.15% ∼ 98.46% embedding compression rates and 20.47% ∼ 34.22% encoder compression rates, while preserving >97.5% semantic parsing performances.We provide the recipe for training and analyze the trade-off between code embedding sizes and downstream performances.
Psychological model of the project investor and manager behavior in risk

All people have to make risky decisions in everyday life. And we do not know how true they are. But is it possible to mathematically assess the correctness of our choice? This article discusses the model of decision making under risk on the example of project management. This is a game with two players, one of which is Investor, and the other is the Project Manager. Each player makes a risky decision for himself, based on his past experience. With the help of a mathematical model, the players form a level of confidence, depending on who the player accepts the strategy or does not accept. The project manager assesses the costs and compares them with the level of confidence. An investor evaluates past results. Also visit the case where the strategy of the player accepts the part.