Reviewerly/exHarmony huggingface dataset:
Formation of special skills and skills of future law enforcement officers at the stage of professional formation

The purpose of the work is theoretical substantiation and experimental verification of the effectiveness of the author's training methodology for future law enforcement officers, aimed at improving the level of special skills.&#x0D; On the basis of the analysis of the peculiarities of the patrol police officers' activity, the current normative documents that regulate the specifics of professional training of future law enforcement officers, requirements for the professional preparedness of law enforcement officers are established. It is ascertained that the professional readiness of the patrol police officer is determined by the armed knowledge of the legislative base, the formation of special motor skills, the development of professionally important features, the availability of practical experience.&#x0D; A thorough study of job situations shows a low, and sometimes insufficient, level of enforcement of police enforcement measures.&#x0D; The effectiveness of the author's training methodology aimed at increasing the level of special police officers' skills development is substantiated and experimentally tested. The essence of innovations is the introduction of specially developed situational tasks, variational situational tasks and complex development of professionally important qualities in the educational process.&#x0D; The study was attended by trainees of the police training course (n=61) of the National Academy of the Interior's Academy of Police Center for Primary Professional Training.&#x0D; In the context of the professional activity of patrol police officers, identifying the level of special skills and skills required to identify three indicators: 1) «Formation of skills and skills in self-defense tactics»; 2) «Formation of skills in fire training»; 3) «Effectiveness of scenario solving».&#x0D; The results of the pedagogical experiment proved the effectiveness of the author's teaching methodology. The use of the proposed innovations contributed to the development of indicators that characterized the special skills and skills of future law enforcement officers. At the end of the pedagogical experiment, a significant difference in the development of the indicators of «Formation of skills and skills in self-defense tactics» (p&lt;0.05) and «Effectiveness of scenario solving» (p&lt;0.001) and unreliable in the indicator of «Formation of skills in fire training» (p&gt;0.05). Prospects for further research are to substantiate the technique of improving the special skills of patrol officers during training in the system of official training.
Task-Robust Model-Agnostic Meta-Learning

Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks. However, these methods only aim to perform well in expectation over tasks coming from some particular distribution that is typically equivalent across meta-training and meta-testing, rather than considering worst-case task performance. In this work we introduce the notion of "task-robustness" by reformulating the popular Model-Agnostic Meta-Learning (MAML) objective [Finn et al. 2017] such that the goal is to minimize the maximum loss over the observed meta-training tasks. The solution to this novel formulation is task-robust in the sense that it places equal importance on even the most difficult and/or rare tasks. This also means that it performs well over all distributions of the observed tasks, making it robust to shifts in the task distribution between meta-training and meta-testing. We present an algorithm to solve the proposed min-max problem, and show that it converges to an $ε$-accurate point at the optimal rate of $\mathcal{O}(1/ε^2)$ in the convex setting and to an $(ε, δ)$-stationary point at the rate of $\mathcal{O}(\max\{1/ε^5, 1/δ^5\})$ in nonconvex settings. We also provide an upper bound on the new task generalization error that captures the advantage of minimizing the worst-case task loss, and demonstrate this advantage in sinusoid regression and image classification experiments.
A Glimpse into the Past, Present, and Future of Engineering Interactive Computing Systems

The area of Engineering Interactive Computing Systems (EICS) is defined as the intersection between Software Engineering (SE) and Human-Computer Interaction (HCI). In this paper, we provide an overview of what EICS is and how it is positioned with respect to other venues in HCI, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to deliver a comparative analysis of the past, present, and perhaps future research and development questions raised in EICS by analyzing the EICS papers published from its first edition to this one, based on global and chronological word clouds, phrase nets, and topic modelling.
PREDICTION OF VELOCITIES OF MODERN VERTICAL MOVEMENTS OF THE EARTH’S CRUST FROM GEODETIC, GEOPHYSICAL AND SEISMOLOGICAL DATA

The paper presents the results of the study aimed at predicting velocities of modern vertical movements of the Earth’s crust, which used the approach developed by Professor G.I. Karataev. This approach is based on mathematical simulation of geological and geophysical phenomena and an axiomatic correlation model for predicting various parameters of the crust from the data on gravitational anomalies. We pioneer in using this approach to study the territory of Belarus on the basis of geodetic, geological, geophysical and seismological data, as well as modern models showing gravitational fields and topography. This paper presents regression equations between the modern vertical movement velocities, gravitational and magnetic fields, crust thickness, and topography data. These equations give reasonably accurate data for constructing a map of forecasted modern vertical movement velocities for the territory of Belarus. Our map proves that the approach based on a correlation model and a complex of geodetic, geological, geophysical and seismological data for predicting the modern vertical movement velocities is very promising and capable of improving the reliability of forecast mapping. It should be noted that other maps of modern vertical crustal movement velocities for territories, including the geostructural elements of different ages and different types, were constructed using the method of simple linear interpolation, which is highly likely to cause prediction errors. In such case, prediction of modern vertical crustal movement velocities should be based on established patterns and correlations between the crustal movements, geophysical fields, and development history of geological structures and their elements.
Bayesian Multiple Changepoint Detection for Stochastic Models in Continuous Time

A multiple changepoint model in continuous time is formulated as a continuous-time hidden Markov model, defined on a countable infinite state space. The new formulation of the multiple changepoint model allows the model complexities, i.e. the number of changepoints, to accrue unboundedly upon the arrivals of new data. Inference on the number of changepoints and their locations is based on a collapsed Gibbs sampler. We suggest a new version of forward-filtering backward-sampling (FFBS) algorithm in continuous time for simulating the full trajectory of the latent Markov chain, i.e. the changepoints. The FFBS algorithm is based on a randomized time-discretization for the latent Markov chain through uniformization schemes, combined with a discrete-time version of FFBS algorithm. It is shown that, desirably, both the computational cost and the memory cost of the FFBS algorithm are only quadratic to the number of changepoints. The new formulation of the multiple changepoint models allows varying scale of run lengths of changepoints to be characterized. We demonstrate the methods through simulations and a real data example for earthquakes.
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers

An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of the art frameworks focus on adversarial attacks for images and other structured model inputs, but not for categorical sequences models. Successful attacks on classifiers of categorical sequences are challenging because the model input is tokens from finite sets, so a classifier score is non-differentiable with respect to inputs, and gradient-based attacks are not applicable. Common approaches deal with this problem working at a token level, while the discrete optimization problem at hand requires a lot of resources to solve. We instead use a fine-tuning of a language model for adversarial attacks as a generator of adversarial examples. To optimize the model, we define a differentiable loss function that depends on a surrogate classifier score and on a deep learning model that evaluates approximate edit distance. So, we control both the adversability of a generated sequence and its similarity to the initial sequence. As a result, we obtain semantically better samples. Moreover, they are resistant to adversarial training and adversarial detectors. Our model works for diverse datasets on bank transactions, electronic health records, and NLP datasets.
Validation and Acceptability of Electronic Intervention Material (EIM) in Grade 7 Biology

Learners at the fore-front of the educative process are expected to acquire relevant skills essential to abreast the demands of the changing world. Recently, the Philippines has acted upon reforms to capacitate the demands of quality education. However, the disparaging condition of the educational arena has resulted to poor students' performance. The Electronic Intervention Material (EIM) is a technology embedded e-strategy to facilitate knowledge acquisition, provide intervention and fill-in learning gaps. The study was undertaken to develop, validate and determine the acceptability of electronic intervention materials in Science for Grade VII. The study made use of experimental method utilizing pretest-posttest design. The findings of the study revealed that the over-all mean scores of the experimental group in the four learning areas remarkably increased during pre-tests and posttests. The developed, validated, and accepted EIM for grade 7 Biology has obtained a very high acceptability (Mean- 4.55, & SD-0.82). When utilized under an experimental test, the students have significantly shown improvement in terms of the student's performance. Significant gain scores in the pre-tests and posttests of the students revealed that the usage of EIM is resulted to improved receptions of concepts and acquired literacy. The EIM can be utilized primarily by teachers teaching the grade 7 whose areas of difficulty were identified on levels of organization/microscopy, plant and animal cells, heredity, inheritance and variation and ecosystem. Such mentioned areas were critical to the appreciation of Biology education, for it serves as fundamental unit of Biology learning in the junior high school Science curriculum.
Investigation of the role of individual’s gender in the design of graphical user interface for mobile learning device

Abstract Nowadays, the mobile phones are making life easier which can do almost anything. Although the concept of mobile learning is not new, there are still studies undergoing about improvement so that users can study using their mobile phones. This project was a study of Graphical User Interface of m-learning applications. The study aimed to investigate whether different genders have different User Interface preferences when came to learn on mobile phones. This project conducted surveys to understand the patterns of User Interface that the users preferred for mobile learning application. The responses had pointed in develop an application based on the survey results which was a unified User Interface design preferred by both males and females. After the application development have been completed, another survey was conducted to evaluate whether the preferred User Interface designs on the application was satisfying them or not. The reviews were mostly positive and thus, a concept or theory of User Interface customization was derived from this project.
Prediction of Short Time Qubit Readout via Measurement of the Next Quantum Jump of a Coupled Damped Driven Harmonic Oscillator

The dynamics of the next quantum jump for a qubit [two level system] coupled to a readout resonator [damped driven harmonic oscillator] is calculated. A quantum mechanical treatment of readout resonator reveals non exponential short time behavior which could facilitate detection of the state of the qubit faster than the resonator lifetime.
Self-Attention Enhanced Patient Journey Understanding in Healthcare System

Understanding patients' journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informative representation that can comprehensively encode hidden dependencies among medical events and its inner entities, and then the use of encoding outputs can greatly benefit the downstream application-driven tasks. A patient journey is a sequence of electronic health records (EHRs) over time that is organized at multiple levels: patient, visits and medical codes. The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes. This paper proposes a novel self-attention mechanism that can simultaneously capture the contextual and temporal relationships hidden in patient journeys. A multi-level self-attention network (MusaNet) is specifically designed to learn the representations of patient journeys that is used to be a long sequence of activities. The MusaNet is trained in end-to-end manner using the training data derived from EHRs. We evaluated the efficacy of our method on two medical application tasks with real-world benchmark datasets. The results have demonstrated the proposed MusaNet produces higher-quality representations than state-of-the-art baseline methods. The source code is available in https://github.com/xueping/MusaNet.
Research on order-preserving encryption scheme based on CryptDB

Abstract With the rapid development of cloud computing technology, cloud database, as an important part of cloud computing services, has gradually become necessary for daily work of enterprises or individuals. However, entrusting data to third-party managing can lead to security issues such as data leakage, and users cannot guarantee data security. To this end, this paper is based on the CryptDB, an open source database encryption proxy system designed by MIT, in this thesis we propose an improvement scheme for the shortcomings of the original CryptDB system. Specific contents including: By studying the CryptDB system, we find that the system is lack of scalability for different databases and does not involve the management of system keys. For the inefficiency of the mutable Order-Preserving Encryption (mOPE) in CryptDB system, we propose an improved additive Order-Revealing Encryption (aORE) scheme by combining the Practical Order-Revealing Encryption (P-ORE) and mOPE. The scheme is based on pseudorandom function and double encryption. Compared with mOPE, it can improve the execution efficiency of the Order-Preserving scheme at the expense of security.
A signature scheme from the finite field isomorphism problem

Abstract In a recent paper the authors and their collaborators proposed a new hard problem, called the finite field isomorphism problem , and they used it to construct a fully homomorphic encryption scheme. In this paper, we investigate how one might build a digital signature scheme from this new problem. Intuitively, the hidden field isomorphism allows us to convert short vectors in the underlying lattice of one field into generic looking vectors in an isomorphic field.
Estudiantes de un plantel educativo secundario del Sur del Ecuador y un Entorno Virtual de Aprendizaje (EVA): Impacto de su implementación

The growth and widespread acceptance of the Internet has led to the creation of Virtual Learning Environments (VLE). To facilitate the integration of VAS from a pedagogical perspective, new technologies should be assessed in an effort to establish whether their introduction has had a beneficial effect on learning outcomes. This research examines middle school students' perceptions of a range of components of VET. The Online Assessment and Opportunity for Improvement in Academic Performance components have the lowest acceptance rates.&nbsp
Continuous and Discrete Wavelet Transforms Associated with Hermite Transform

In this paper, we accomplished the concept of continuous and discrete Hermite wavelet transforms. We also discussed some basic properties of Hermite wavelet transform. Inversion formula and Parsevals formula for continuous Hermite wavelet transform is established. Moreover the discrete version of wavelet transform is discussed.
Automatic Controlled Wheel Chair Using Multi-Intelligence Technique

Tremendous magnification in medical technology is incrementing day by day with excellent remedying methodologies and making persons to be salubrious, but paralysis disease is unable to remedy perpetually whereas partial remedying is possible. Peoples are affected with plenarily and/or quarterly paralyzed persons were unable to survive themselves, even though with felicitous medication is available. Paralyzed persons need an opportune conveyance to move themselves independently for their personal needs. Now a day’s advancement of automation in electric conveyance designs is predicated on the desideratum of application. In this paper, a new wheelchair design methodology was presented with multi controlled schemes for the fully or partially paralyzed persons to move independently without help of any caretaker. Three controlling schemes were developed and integrated to make the robust of the wheelchair design. A prototype model is developed and presents in the paper, it was tested rigorously to analyze the performance of the proposed methodology. Results show the efficacy of the method.
Digital Technologies as antecedents to Process Integration and Dynamic Capabilities in Healthcare: An Empirical Investigation

Healthcare has been in focus over the past decade due to its criticality and continuous revolution.In this digital era, with the advent of various technologies, healthcare is undergoing a massive transformation.This study attempts to analyze the impacts of three major digital technologies which are being adopted in the healthcare sector which are electronic medical records (EMR), enterprise resource planning (ERP) and internet-of-things (IoT) enabled medical wearables in the hospital context.Focusing towards analyzing the impact of these technologies towards process-integration and further towards dynamic capabilities like quality, agility and responsiveness; the study framework is well-grounded by two theoretical-underpinnings -Cybernetic Control theory and Dynamic Capability theory.Questionnaire has been finalized through extant literature review and focused-group-discussion.Empirical investigation has been done with a sample of 154 hospital respondents from four major metro cities from the private tertiarycare hospitals in India.The empirical analysis of the framework is carried out by exploratory-factor-analysis, confirmatory-factor-analysis and structural-equationmodelling.The study outcomes highlight linkages both from technologies to process-integration and further towards dynamic capabilities.This study uniformly
Research on access control method of Digital Archives based on blockchain

Abstract This paper analyzes the security problems that need to be solved urgently in digital archives system: First, the centralized storage of digital archives makes it overwhelmingly dependent on the third-party storage organization. Second, every transaction has its own access proof and its own authorization proof, it’s not transparent to others. Then the blockchain technology is introduced to solve the existing problems. We store the digital file attachment and file attributes in the private IPFS cluster after encryption. The address of IPFS and digital fingerprint of the file are stored on the private blockchain. Additionally, this paper stores and checks the height and hash value of the latest irreversible block of the private blockchain, to guarantee the authenticity of data on private blockchain. To solve the second problem, we store the ABAC on public blockchain, add time and position information to the attributes of the subject, to achieve fine-grained access control. Finally, this model is implemented on fabric1.0.3.
Performance Evaluation of English Part of Speech Tagging Based on Multi-feature Knowledge Algorithm

Abstract In order to solve the traditional English part-of-speech tagging methods, a variety of English part-speech tagging methods are introduced. This paper systematically studies the method of multi-feature knowledge algorithm to mark English part of speech, then demonstrates it theoretically, and gives some examples to illustrate the characteristics of this method, and then evaluates it objectively.
Prediction of Solar Eclipses using Extreme Gradient Boost Algorithm

An eclipse is an event that obscures the light of the Moon when it is in Earth's shadow or the light of the Sun when the Moon is between it and the Earth. An eclipse occurs when there is a full moon or a new moon. To make exact eclipse predictions, you would need to calculate the precise motions of the sun and moon, and that requires a computer and proper software. You can predict eclipses by understanding the conditions that make them possible. There are two conditions for an eclipse: The sun must be crossing a node, and the moon must be crossing either the same node (solar eclipse) or the other node (lunar eclipse). That means, of course, that solar eclipses can occur only when the moon is new, and lunar eclipses can occur only when the moon is full.
A manifesto for increasing access to data in engineering

Abstract This paper introduces a set of principles that articulate a shared vision for increasing access to data in the engineering and related sectors. The principles are intended to help guide progress toward a data ecosystem that provides sustainable access to data, in ways that will help a variety of stakeholders in maximizing its value while mitigating potential harms. In addition to being a manifesto for change, the principles can also be viewed as a means for understanding the alignment, overlaps and gaps between a range of existing research programs, policy initiatives, and related work on data governance and sharing. After providing background on the growing data economy and relevant recent policy initiatives in the United Kingdom and European Union, we then introduce the nine key principles of the manifesto. For each principle, we provide some additional rationale and links to related work. We invite feedback on the manifesto and endorsements from a range of stakeholders.
Link Prediction between Group Entities in Knowledge Graphs (Student Abstract)

Link prediction in knowledge graphs (KGs) aims at predicting potential links between entities in KGs. Existing knowledge graph embedding (KGE) based methods represent individual entities and links in KGs as vectors in low-dimension space. However, these methods focus mainly on the link prediction of individual entities, yet neglect that between group entities, which exist widely in real-world KGs. In this paper, we propose a KGE based method, called GTransA, for link prediction between group entities in a heterogeneous network by integrating individual entity links into group entity links during prediction. Experiments show that GTransA decreases mean rank by 5.4%, compared to TransA.