Reviewerly/exHarmony huggingface dataset:
Psychological Make-up of Corrupt Officials Serving their Sentences in Confinement Institutions

The article discusses the psychological make-up of corrupt officials serving their prison sentences in confinement institutions. We established that they are distinguished by the following qualities: attentiveness and persistence in attaining their personally significant goals, lack of straightforwardness in communication, machiavellism, distinct deviousness, slyness, insidiousness, cynicism, estrangement, emotional indifference, disregard for moral and ethical standards, tendency to manipulate and need to dominate others, aspiration for high social standing. The empirical study that we conducted in the places of imprisonment helped us to reveal the six-factor personality structure of a corrupt official. By the usage of cluster analysis the corrupted officials have been diagnosed to be of the venal personality type. On the one hand, individuals of this type are distinguished by the pronounced domination factor, on the other hand, the adequacy of their self-appraisal is low.
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning

Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detection approaches: (1) many approaches perform well on low-dimensional problems however the performance on high-dimensional instances, such as images, is limited; (2) many approaches often rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency. We implement this idea and evaluate its performance for anomaly detection. Our experiments with three datasets show that OIAD can detect over 90% of anomalies while maintaining a low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.
Towards a Sustainable Future: Ubiquitous Knowledge Mixed Reality Museum

An Intelligent Museum built under the Ubiquitous Computing paradigm, where services follow the user seamlessly across different environments, emerges as a new type of digital institution present beyond its physical limits: the Ubiquitous Museum. Mixed Reality provides the means of harvesting knowledge from memory institutions and smart devices connected to the Internet of Things. At the same time, it enables embedding multimodal contents from the Museum across the Intelligent Environment. We research the user experience of knowledge dissemination through the Ubiquitous Museum. We exemplify how our work aims to help achieve this. We also discuss how using embedded Museum contents helps sustainable development.
Overview of TASS 2020: Introducing Emotion Detection

The Task on Semantic Analysis at SEPLN (TASS task within IberLEF 2020 workshop) took place on September 22, reaching its ninth edition Due to the COVID-19 pandemic, the number of participants is lower compared to past campaigns Also, the organizers decided to held it remotely In this edition, the classical polarity classification subtask was, again, organized As a novelty, a second subtask was proposed to foster research in emotion detection of Spanish texts on a new dataset This paper summarizes the different approaches of the teams who participated, the key insights of their systems and the results obtained for all the proposed solutions © 2020 Copyright for this paper by its authors Use permitted under
Unsupervised geochemical classification and automatic 3D mapping of the Bolshetroitskoe high-grade iron ore deposit (Belgorod Region, Russia)

Abstract We stated and solved three successive problems concerning automatization of geological mapping using the case of the Bolshetroitskoe high-grade iron ore deposit in weathered crust of Banded Iron Formation (Kursk Magnetic Anomaly, Belgorod Region, Russia). (1) Selecting a classification (clustering) method of geochemical data without reference sampling, i.e., solution of an “unsupervised clustering task”. We developed 5 rock classifications based on different principles, i.e., classification by visual description, by distribution of economic component (Fe 2 O 3 ), by cluster analysis of raw data and centered log-ratio transformation of the raw data, and by artificial neural network (Kohonnen’s self-organized map). (2) Non-parametric comparison of quality of the classifications and revealing the best one. (3) Automatic 3D geological mapping in accordance with the best classification. The developed approach of automatic 3D geological mapping seems to be rather simple and plausible.
ProtoAttend: Attention-Based Prototypical Learning

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes. Our method, ProtoAttend, can be integrated into a wide range of neural network architectures including pre-trained models. It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes. The resulting model outperforms state of the art in three high impact problems without sacrificing accuracy of the original model: (1) it enables high-quality interpretability that outputs samples most relevant to the decision-making (i.e. a sample-based interpretability method); (2) it achieves state of the art confidence estimation by quantifying the mismatch across prototype labels; and (3) it obtains state of the art in distribution mismatch detection. All this can be achieved with minimal additional test time and a practically viable training time computational cost.
New extremal self-dual binary codes of length 68 via composite construction, &amp;Fopf;&lt;SUB align="right"&gt;2 + &lt;i&gt;u&lt;/i&gt;&amp;Fopf;&lt;SUB align="right"&gt;2 lifts, extensions and neighbours

We describe a composite construction from group rings where the groups have orders 16 and 8. This construction is then applied to find the extremal binary self-dual codes with parameters [32, 16, 8] or [32, 16, 6]. We also extend this composite construction by expanding the search field which enables us to find more extremal binary self-dual codes with the above parameters and with different orders of automorphism groups. These codes are then lifted to
From Syntactic Structure to Semantic Relationship: Hypernym Extraction from Definitions by Recurrent Neural Networks Using the Part of Speech Information

The hyponym-hypernym relation is an essential element in the semantic network. Identifying the hypernym from a definition is an important task in natural language processing and semantic analysis. While a public dictionary such as WordNet works for common words, its application in domain-specific scenarios is limited. Existing tools for hypernym extraction either rely on specific semantic patterns or focus on the word representation, which all demonstrate certain limitations. Here we propose a method by combining both the syntactic structure in definitions given by the word’s part of speech, and the bidirectional gated recurrent unit network as the learning kernel. The output can be further tuned by including other features such as a word’s centrality in the hypernym co-occurrence network. The method is tested in the corpus from Wikipedia featuring definition with high regularity, and the corpus from Stack-Overflow whose definition is usually irregular. It shows enhanced performance compared with other tools in both corpora. Taken together, our work not only provides a useful tool for hypernym extraction but also gives an example of utilizing syntactic structures to learn semantic relationships (Source code and data available at https://github.com/Res-Tan/Hypernym-Extraction ).
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows

Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks. In this regard, the study of powerful attack models sheds light on the sources of vulnerability in these classifiers, hopefully leading to more robust ones. In this paper, we introduce AdvFlow: a novel black-box adversarial attack method on image classifiers that exploits the power of normalizing flows to model the density of adversarial examples around a given target image. We see that the proposed method generates adversaries that closely follow the clean data distribution, a property which makes their detection less likely. Also, our experimental results show competitive performance of the proposed approach with some of the existing attack methods on defended classifiers.
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm

The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations. This paper introduces a novel EM algorithm, called SPIDER-EM, for inference from a training set of size n, n ≫ 1. At the core of our algorithm is an estimator of the full conditional expectation in the E-step, adapted from the stochastic path-integrated differential estimator (SPIDER) technique. We derive finite-time complexity bounds for smooth non-convex likelihood: we show that for convergence to an ǫ-approximate stationary point, the complexity scales as K Opt (n, ǫ) = O(ǫ −1) and K CE (n, ǫ) = n + √ nO(ǫ −1), where K Opt (n, ǫ) and K CE (n, ǫ) are respectively the number of M-steps and the number of per-sample conditional expectations evaluations. This improves over the state-of-the-art algorithms. Numerical results support our findings.
Compositional Phrase Alignment and Beyond

Phrase alignment is the basis for modelling sentence pair interactions, such as paraphrase and textual entailment recognition.Most phrase alignments are compositional processes such that an alignment of a phrase pair is constructed based on the alignments of their child phrases.Nonetheless, studies have revealed that non-compositional alignments involving long-distance phrase reordering are prevalent in practice.We address the phrase alignment problem by combining an unordered tree mapping algorithm and phrase representation modelling that explicitly embeds the similarity distribution in the sentences onto powerful contextualized representations.Experimental results demonstrate that our method effectively handles compositional and non-compositional global phrase alignments.Our method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators.
Neurosymbolic Reinforcement Learning with Formally Verified Exploration

We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces. A key challenge for provably safe deep RL is that repeatedly verifying neural networks within a learning loop is computationally infeasible. We address this challenge using two policy classes: a general, neurosymbolic class with approximate gradients and a more restricted class of symbolic policies that allows efficient verification. Our learning algorithm is a mirror descent over policies: in each iteration, it safely lifts a symbolic policy into the neurosymbolic space, performs safe gradient updates to the resulting policy, and projects the updated policy into the safe symbolic subset, all without requiring explicit verification of neural networks. Our empirical results show that Revel enforces safe exploration in many scenarios in which Constrained Policy Optimization does not, and that it can discover policies that outperform those learned through prior approaches to verified exploration.
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures. Our method works in a steepest descent fashion, which iteratively finds the best network within a functional neighborhood of the original network that includes a diverse set of candidate network structures. By using Taylor approximation, the optimal network structure in the neighborhood can be found with a greedy selection procedure. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures that avoid catastrophic forgetting in continual learning. Empirically, firefly descent achieves promising results on both neural architecture search and continual learning. In particular, on a challenging continual image classification task, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
Bayesian Bits: Unifying Quantization and Pruning

We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition of the quantization operation, which sequentially considers doubling the bit width. At each new bit width, the residual error between the full precision value and the previously rounded value is quantized. We then decide whether or not to add this quantized residual error for a higher effective bit width and lower quantization noise. By starting with a power-of-two bit width, this decomposition will always produce hardware-friendly configurations, and through an additional 0-bit option, serves as a unified view of pruning and quantization. Bayesian Bits then introduces learnable stochastic gates, which collectively control the bit width of the given tensor. As a result, we can obtain low bit solutions by performing approximate inference over the gates, with prior distributions that encourage most of them to be switched off. We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks that provide a better trade-off between accuracy and efficiency than their static bit width equivalents.
Development of a Secure and Private Electronic Procurement System based on Blockchain Implementation

This paper presents the development of an online procurement system and the integration of blockchain technology.Various tools such as PHP, JavaScript, HTML, CSS, and jQuery were used in designing the graphical, programming logic, and blockchain aspect of the system.Every page and function will have their respective construction and result.In addition, the proposed system's flow of process and the methods on the testing and hosting of the site as well as the different web development languages used in every part of the development and design process were presented.The proposed system was successfully and functionally developed starting from the execution of procurement proper, to the placement of procured items or goods, and up to the signing of contracts by the winner and the procurer.Lastly, features were added such as user profiles of the bidder and procurer.
Students and the Risk of Virtual Relationships in Social Media: Improving Learning Environments

This study aimed to analyze the risks of social virtual relationships in social media on students. The study's sample consisted of 200 students from the Ajman University (UAE). The study used a questionnaire survey to identify the risks of virtual relationships. The finding was a moderate risk of virtual communication in social media affecting youth. The study offered recommendations to reduce the virtual communication risk through the cumulative effort of family and university, leisure-time activities, and awareness-rising classes.
Storing, preprocessing and analyzing tweets: finding the suitable noSQL system

In the past few years, Tweets have been widely used to perform Big Data analysis. However, the incredible amount of data captured by Twitter needs to be stored for further processing which may be a challenging task for many database systems. NoSQL is a generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is challenging. Based on these motivations, this work is carried out to find the suitable NoSQL system to manage Tweets. This paper presents the requirements of managing Tweets and provides a detailed comparison of five NoSQL systems namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j regarding these requirements. The five NoSQL systems are compared in a real scenario where we collect and analyze 1.000.000 Tweets. The chosen scenario enables to evaluate not only the performance of the read and write operations, but also other requirements related to Tweets management such as scalability, analysis tools support and analysis languages support. The obtained results show that Couchbase is the most suitable NoSQL systems for managing Tweets.
Knowing when to ask: sound scheduling of name resolution in type checkers derived from declarative specifications

There is a large gap between the specification of type systems and the implementation of their type checkers, which impedes reasoning about the soundness of the type checker with respect to the specification. A vision to close this gap is to automatically obtain type checkers from declarative programming language specifications. This moves the burden of proving correctness from a case-by-case basis for concrete languages to a single correctness proof for the specification language. This vision is obstructed by an aspect common to all programming languages: name resolution. Naming and scoping are pervasive and complex aspects of the static semantics of programming languages. Implementations of type checkers for languages with name binding features such as modules, imports, classes, and inheritance interleave collection of binding information (i.e., declarations, scoping structure, and imports) and querying that information. This requires scheduling those two aspects in such a way that query answers are stable—i.e., they are computed only after all relevant binding structure has been collected. Type checkers for concrete languages accomplish stability using language-specific knowledge about the type system. In this paper we give a language-independent characterization of necessary and sufficient conditions to guarantee stability of name and type queries during type checking in terms of critical edges in an incomplete scope graph . We use critical edges to give a formal small-step operational semantics to a declarative specification language for type systems, that achieves soundness by delaying queries that may depend on missing information. This yields type checkers for the specified languages that are sound by construction—i.e., they schedule queries so that the answers are stable, and only accept programs that are name- and type-correct according to the declarative language specification. We implement this approach, and evaluate it against specifications of a small module and record language, as well as subsets of Java and Scala.
Concordancing in writing pedagogy and CAF measures of writing

Abstract Since the early twenty-first century, data-driven learning (DDL) approach that is a pedagogical application of corpus linguistics in classroom, has introduced a paradigm shift in EFL instruction. Research output, however, concerning this inductive, discovery-oriented learning is equivocal. This study, thus, explored the application of both native-speaker and local learner corpora, attesting the effect of direct vs. indirect DDL activities on 39 EFL learners’ development in CAF measures of writing. To this end, two experimental groups were taught through corpus consultation, but the control group received the conventional method of using a textbook, teacher explanations, and classroom exercises. Results obtained from three (two experimental and one control) groups of participants’ writing performances pre and post to seven sessions of paragraph writing confirmed the significant role of indirect DDL in writing more accurate and fluent paragraphs; however, no statistical evidence was found as regards syntactic complexity. Moreover, no significant effect of the direct DDL method in improving learners’ writing was observed, which is, thus, interpreted as suggestive that applying indirect DDL could be more effective than the direct DDL approach. It is concluded that classroom-based computers are not necessarily essential tools to implement the DDL pedagogy.
THE MAIN APPROACHES TO UNDERSTANDING DIGITALISATION AND DIGITAL VALUES

The article reveals the main approaches to the concept of «digitalisation» in Russian and foreign literature. It appeared in the 1970s, and since 2013, due to transferring digital business models into economy, the fifth stage of digitalisation has begun. In education that stage is characterised by using in educational processes models and technologies of digital cooperation and production of services, which leads to reformatting and updating of education. At the same time, there is a problem of understanding the essence of digitalisation and giving its definition. In accordance with the specifics of denining digitalisation, the authors consider two approaches to be applicable to digitalisation in educational sphere – application of digital resources in education (technological integration, when digitalisation is designed to optimise the educational processes with help of digital technologies); digitalisation of the curriculum and educational goals (including digital literacy in national educational standards; in that meaning digitalisation is meant to teach students to work with digital technologies and develop their digital competences). The authors of the article consider digital processes in education as necessary for transition to the digital economy and training of appropriate personnel. Digitalisation is recognised as a positive phenomenon changing the organisation of teaching processes and teacher education in accordance with the demands of time.
A Hybrid Image Encryption Algorithm Based on Chaos System and Simplified Advanced Encryption System

Image encryption algorithms usually have two processes: permutation and diffusion. In order to enhance the performance of the permutation process, this study proposes a dynamic permutation method based on XOR operation and sorting. Combined with the hyper-chaotic system, SHA-256, the proposed permutation method and the simplified advanced encryption system (S-AES), a novel image encryption algorithm is presented in this paper. Firstly, the SHA-256 is used to generate initial condition values of the chaotic system and control parameters of the permutation method. Then, the chaotic system generates pseudo-random numbers, which are used in the permutation method and S-AES. Finally, the proposed permutation method and S-AES are used to encrypt the plain-image. In particular, parallel computing is used in this study to accelerate encryption speed. The results and analysis indicate that the proposed algorithm has excellent security to resist various typical attacks and fast encryption speed.