Reviewerly/exHarmony huggingface dataset:
EXPLAINING AI MODELS FOR CLINICAL RESEARCH: VALIDATION THROUGH MODEL COMPARISON AND DATA SIMULATION

For clinical research to take advantage of artificial intelligence techniques such as the various types of deep neural networks, we need to be able to explain the deep neural network models to clinicians and researchers.While some explanation approaches have been developed, their validation and utilization are very limited.In this study, we evaluated a novel explainable artificial intelligence method called impact assessment by applying it to deep neural networks trained on real world and simulated data.Using real clinical data, the impact scores from deep neural networks were compared with odds ratios from logistic regression models.Using simulated data, the impact scores from deep neural networks were compared with the impact scores calculated based on the ground truth (i.e.formulas used to generate the simulated data).The correlations between impact scores and odds ratios ranged from 0.63 to 0.97.The correlations between impact scores from DNN and ground truth ranged were all above 0.99.These suggest that the impact score provide a valid explanation of the contribution of a variable in a DNN model.
Student learning strategies higher level

The objective of this work is to know the type of learning strategies used by the students of the Primitive and National School of San Nicolás de Hidalgo. The teachers of upper middle level mostly consider that the students learned to use these strategies the previous years, from their training. For this reason, regardless of the grade level that the teacher teaches, the teacher must make a diagnosis of what strategies they can handle that develops their learning, or teach these strategies to their students so that they can assimilate the content autonomously. It is a crosssectional, descriptive and correlational investigation. A sample was managed, made up of 378 students enrolled in the second and fourth semester of upper secondary education in the February / August 2020 school year. The research was achieved through the Assessment Questionnaire of Learning Strategies for University Students (CEVEAUPEU), instituted by Gargallo, SuárezRodríguez and Pérez-Pérez (2009). 1.9% have regular study strategies, 75.4% have good study strategies and finally 21.7% have excellent strategies. The results allow to conclude that the participants demand greater knowledge and use of the strategies of processing and use of the information EEP (grouped) related to the search, selection, processing and use of the information, so essential in this time when it exceeds the information, as a result of technological and scientific advances.
Exploring meaningful visual effects and quantities of interest from dynamic models through dynamac

dynamac (2020) implements a framework of estimating and interpreting autoregressive distributed lag (ARDL) models in R. dynamac uses stochastic simulation techniques (Jordan & Philips, 2018a, 2018b) to easily recover traditional quantities of interest, such as short-and long-run effects, even from complex dynamic specifications, including models with cointegration.These simulation techniques bring a wider set of inferences from complex models to users in fields as diverse as environmental science (Danish, 2020) and economics (Sharma, 2019).Users can make dynamic inferences from their models, including measures of uncertainty, without the need for any formulae or algebraic solutions.Although other packages may estimate ARDL models (e.g., dynlm (Zeileis, 2019), which only provides model estimates; ardl (Natsiopoulos & Tzeremes, 2020), which uses dynlm for estimation but provides support for the bounds test for cointegration and automated lag selection; dlagM (Demirhan, 2020), which allows for forecasting), dynamac is unique in that it provides post-estimation diagnostics for autocorrelation in the residuals and is designed specifically for providing inferences through counterfactual simulation.
Comprehensive Online Network Pruning via Learnable Scaling Factors.

One of the major challenges in deploying deep neural network architectures is their size which has an adverse effect on their inference time and memory requirements. Deep CNNs can either be pruned width-wise by removing filters based on their importance or depth-wise by removing layers and blocks. Width wise pruning (filter pruning) is commonly performed via learnable gates or switches and sparsity regularizers whereas pruning of layers has so far been performed arbitrarily by manually designing a smaller network usually referred to as a student network. We propose a comprehensive pruning strategy that can perform both width-wise as well as depth-wise pruning. This is achieved by introducing gates at different granularities (neuron, filter, layer, block) which are then controlled via an objective function that simultaneously performs pruning at different granularity during each forward pass. Our approach is applicable to wide-variety of architectures without any constraints on spatial dimensions or connection type (sequential, residual, parallel or inception). Our method has resulted in a compression ratio of 70% to 90% without noticeable loss in accuracy when evaluated on benchmark datasets.
ANCAMAN KEAMANAN JARINGAN PADA SERVER UNTUK MEMBATASI WEBSITE TERTENTU MENGGUNAKAN MIKROTIK

&lt;p&gt;&lt;em&gt;Mikrotik RouterOs ™ is an operating system and software that can be used to make a computer a reliable network router, designed to provide convenience for its users, the administration can be done through Windows application (Winbox), The server is a computer system that provides certain types of services (services) in a computer network, the server is supported by a processor that is scalable and a large ram is also equipped with a special operating system, called a (network operating system). The internet provides a lot of useful content if the user uses the internet well, besides that the&lt;strong&gt; &lt;/strong&gt;internet can also have a negative impact on its users if the internet is used for negative things which will affect the young generation who will embrace this country to be the successor of this nation. outside the prevailing norms. Today the internet has many different types of social networking sites that have been used by many people, the goal or One of the solutions is that internet users do not access the negative or self-detrimental effects, using the Scientific of Inquiry method by limiting access to websites Specifically for prevention efforts in accessing negative internet sites in this case containing pornographic content is by blocking these negative sites permanently using a web proxy that serves to block some websites that cannot be accessed by clients through a browser on the Mikrotik Faculty of Engineering routers Ibn Khaldun Universit&lt;/em&gt;&lt;/p&gt;
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components

We propose Deep Autoencoding Predictive Components (DAPC) -- a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predictive information of latent feature sequences, which is the mutual information between past and future windows at each time step. In contrast to the mutual information lower bound commonly used by contrastive learning, the estimate of predictive information we adopt is exact under a Gaussian assumption. Additionally, it can be computed without negative sampling. To reduce the degeneracy of the latent space extracted by powerful encoders and keep useful information from the inputs, we regularize predictive information learning with a challenging masked reconstruction loss. We demonstrate that our method recovers the latent space of noisy dynamical systems, extracts predictive features for forecasting tasks, and improves automatic speech recognition when used to pretrain the encoder on large amounts of unlabeled data.
THE UNCERTAIN DECORUM OF ONLINE IDENTIFICATION: A STUDY IN QUALITATIVE INTERVIEWS

This study explores the ethics and motivations of online identification—how and why people collect and publish identifying information about others online. In seven interviews, activists, Internet users, advocates, and journalists were asked about their investigative practice and how they viewed the ethics of deanonymization. Using ethnographic interviewing techniques and a thematic analysis inspired by grounded theory, I describe respondents’ investigations and compare them to existing theories in surveillance studies, online anonymity, and digital vigilantism. Respondents often struggled with making their work accessible and impactful in an ethical manner. They obfuscated irrelevant information that might incite online harassment and took care in who they collaborated with. The respondents also debated what to do when people misinterpreted their work or thought that they had acted unjustly. The precautions they incorporated into their publications are examples of how people navigate online ethics when there isn’t a clear standard for moral decisions. Ultimately, the interview results did not follow models of digital vigilantism and doxxing, and I caution against using those terms to apply to cases like those described in this study. I also make suggestions for how these results could augment theoretical models of anonymity, particularly how respondents’ investigative techniques and backgrounds lead them to different moral commitments.
Short Paper: Privacy Comparison of Contact Tracing Mobile Applications for COVID-19

With the COVID-19 pandemic, quarantines took place across the globe. In the aim of stopping or slowing the progression of the COVID-19 contamination, many countries have deployed a contact tracing system to notify persons that be in contact with a COVID-positive person. The contact tracing system is implemented in a mobile application and leverages technologies such as Bluetooth to trace interactions between persons. This paper discusses different smart-phone applications based on contact tracing system from privacy point of view.
Automated written corrective feedback for supporting students and instructors across curriculum

Grammarly is a cloud-based writing enhancement tool that is being piloted at ISU in writing-focused and writing-intensive courses across seven different departments. The presentation discusses the strengths and shortcomings of Grammarly and suggestions for curriculum integration.
Homogeneous open quantum walks on the line: criteria for site recurrence and absorption

In this work, we study open quantum random walks, as described by S. Attal et al. These objects are given in terms of completely positive maps acting on trace-class operators, leading to one of the simplest open quantum versions of the recurrence problem for classical, discrete-time random walks. This work focuses on obtaining criteria for site recurrence of nearest-neighbor, homogeneous walks on the integer line, with the description presented here making use of recent results of the theory of open walks, most particularly regarding reducibility properties of the operators involved. This allows us to obtain a complete criterion for site recurrence in the case for which the internal degree of freedom of each site (coin space) is of dimension 2. We also present the analogous result for irreducible walks with an internal degree of arbitrary finite dimension and the absorption problem for walks on the semi-infinite line.
APLIKASI CMS WORDPRESS UNTUK MERANCANG WEBSITE SEBAGAI MEDIA PROMOSI PADA SEKAR ARUM CATERING SIDOARJO

The purpose of this research was to design a website using Wordpress CMS as a promotion media Sekar Arum Catering to increase its sales. This research was an action research. The data collection methods were interviews, questionnaires, observation, and documentation. The website was tested by distributing questionnaires to 2 computer experts , 2 marketing experts, and 6 public users. The questionnaires for the computer experts were developed based on the WebQual theory to analyze the website design. The questionnaires distributed to marketing experts and the public users were developed based on the EPIC theory used to analyze the information and services offered . The results showed that the website designed for Sekar Arum Catering could be used as a promotion media. The results of the questionnaire distributed to the computer experts showed a score of 77.1% on a Likert Scale . The questionnaires distributed to the marketing experts The average score of those four dimensions were 3.75 75 and belonged to an effective categor y. As for the questionnaires distributed to the public users showed scores average score of those four dimensions were 3.67 and belonged to an effective category .
Future Graduate Salaries Prediction Model Based On Recurrent Neural Network

Prediction models are widely applied in several fields.In this study we present a discussion on using Recurrent Neural Network as predictor for salaries of future graduates.The model is based on feature analysis which leads to input values of the predictor.We have analyzed several compositions and ideas.As a result we have selected Recurrent Neural Network to be the most accurate.Presented results confirm this selection and show high precision.
Vogons, the Invaders

Game development is a recognized and documented approach toimproving computer programming learning. This article describesthe redesign of the popular game Space Invaders, using Python asthe programming language and libraries Arcade, Random, Os andMath. The game developed has modifications in various aspects, liketextures, jogability, scores and story. The motivation for the remakewas the desire to bring a nostalgic experience for the player,combined with learning about game development. Considering thatit was built by people with no previous experience in gamedevelopment, the proposed objectives were achieved, with thegame having been completed. The Arcade library proved to be easyto use and productive. As future work, an online ranking will bedeveloped, in addition to generating bonuses for the player and theuse of design patterns.
Sistem Informasi Monitoring dan Evaluasi Proyek dengan Menerapkan Perhitungan Deviasi

The process of monitoring and evaluating a project needs to be done to ensure the project runs smoothly. The problems that arise at this stage are obstacles to knowing the progress of the ongoing project. Some other obstacles are the potential for file loss, duplication of data, human errors, the need for fast processing, and the need for data integrity of two systems. This study aims to develop a project monitoring and evaluation information system applying deviation calculations. This calculation is carried out to see the realization of the work of an ongoing project. The system design technique uses the use case diagram method which describes the process of the system. The waterfall method is used to develop a monitoring and evaluation information system for this project. The research result is in the form of a project monitoring and evaluation information system that can provide information about the work realization of an ongoing project.Keywords:Monitoring, Evaluation, Deviation Calculation, Information System.
Are Words Commensurate with Actions? Quantifying Commitment to a Cause from Online Public Messaging

Public entities such as companies and politicians increasingly use online social networks to communicate directly with their constituencies. Often, this public messaging is aimed at aligning the entity with a particular cause or issue, such as the environment or public health. However, as a consumer or voter, it can be difficult to assess an entity's true commitment to a cause based on public messaging. In this paper, we present a text classification approach to categorize a message according to its commitment level toward a cause. We then compare the volume of such messages with external ratings based on entities' actions (e.g., a politician's voting record with respect to the environment or a company's rating from environmental non-profits). We find that by distinguishing between low- and high- level commitment messages, we can more reliably identify truly committed entities. Furthermore, by measuring the discrepancy between classified messages and external ratings, we can identify entities whose public messaging does not align with their actions, thereby providing a methodology to identify potentially "inauthentic" messaging campaigns.
Communicating vital aspects of cybersecurity in the nonprofit sector

By Sabre Campbell, Communication; Taylor Mock, Communication; Claire Pazynski, Communication Advisor: Michael Sharp Abstract: City Gospel Mission is a nonprofit organization in Cincinnati working to reduce homelessness and poverty. We are working with City Gospel Mission to aid in the communication process of informing staff and participants of the dos and don'ts of their online processes. Working with the IT manager of City Gospel Mission, we are creating a newsletter that will be shared monthly to effectively communicate why cybersecurity protocols are important, the implications if they are not met, and how the individual can ensure that they are. It is crucial that information regarding the stakeholders and donators of City Gospel Mission remains secure to ensure their trust and the continuation of their services. This remains true for the entirety of the nonprofit sector. Our process of researching and relaying information, ideally, can be successfully reinstated in other contexts as well.
Quantum Computing Solution of DC Power Flow

In this paper, we model and solve a fundamental power system problem, i.e., DC power flow, using a practical quantum computer. The Harrow-Hassidim-Lloyd (HHL) quantum algorithm is used to solve the DC power flow problem. The HHL algorithm for the solution of a system of linear equations (SLE) offers an exponential speedup over the classical computers. The speedup advantage is more significant when the size and the frequency of solving the power flow problem become more substantial. Verified quantum computing applications to power systems are merely nonexistent at this point. Through this paper, we plan to (1) provide a proof-of-concept that practical power system problems can be solved using quantum technology, (2) build a quantum-grid foundation by solving a fundamental power system problem with applications in many other critical grid problems, and (3) apply HHL to solve an SLE which has broad applications in many power system problems. A small 3-bus system is used for testing and demonstration purposes, considering the limitations of the available quantum computing hardware and software. The proposed method's merits and effectiveness are demonstrated using IBM open-source quantum computer and reported through proof-of-concept experimental demonstration using a 4-qubit quantum information processor.
A Short Introduction to PF: A C++ Library for Particle Filtering

The PF library provides class and function templates that offer fast implementations for a variety of particle filtering algorithms.Each of these algorithms is useful for a wide range of time series models.In this library, each available particle filtering algorithm is provided as an abstract base class template.Once the data analyst has a specific state-space or hidden Markov model in mind, she will pick which type(s) of particle filtering algorithm(s) to associate with that model by including the appropriate header file.For each model-particle filter pair, she will write a class template for her model that inherits from the particle filter's base class template.
Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering

This paper introduces Multi-Level feature learning alongside the Embedding layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering. We use agglomerative clustering as the multi-level feature learning that provides a hierarchical structure on the latent feature space. It is shown that applying multi-level feature learning considerably improves the basic deep convolutional embedding clustering (DCEC). CAE-MLE considers the clustering loss of agglomerative clustering simultaneously alongside the learning latent feature of CAE. In the following of the previous works in inverse feature learning, we show that the representation of learning of error as a general strategy can be applied on different deep clustering approaches and it leads to promising results. We develop deep inverse feature learning (deep IFL) on CAE-MLE as a novel approach that leads to the state-of-the-art results among the same category methods. The experimental results show that the CAE-MLE improves the results of the basic method, DCEC, around 7% -14% on two well-known datasets of MNIST and USPS. Also, it is shown that the proposed deep IFL improves the primary results about 9%-17%. Therefore, both proposed approaches of CAE-MLE and deep IFL based on CAE-MLE can lead to notable performance improvement in comparison to the majority of existing techniques. The proposed approaches while are based on a basic convolutional autoencoder lead to outstanding results even in comparison to variational autoencoders or generative adversarial networks.
Massive parallelism Fourier-optic convolutional processor

Here we report a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel showing an MNIST and CIFAR-10 accuracy of 96% and 54%, respectively.
The Basic Principles of Teaching English to Elderly Students within the Framework of “Lifelong Learning”

The article considers methodological peculiarities of teaching English to “third age” students on the basis of higher education institutions. The research objectives are as follows: to reveal the basic methodological principles of teaching this age group taking into account age-related and psychological features of elderly people, to develop a methodological approach to choosing relevant content of the educational program. Scientific originality of the study involves identifying the basic criteria for choosing the content of the English-language course for “third age” people. The paper provides examples of exercises used in English classes for elderly Muscovites. The authors conclude that foreign language teaching within the framework of “Lifelong Learning” should be based on four methodological principles - approximation, relevance, cyclicity and availability.
A Python package for data assimilation in the eWatercycle program &#8211; a hydrological framework

&amp;lt;p&amp;gt;The eWaterCycle program provides a collaborative environment for hydrological modelers, developed by the Netherlands eScience Center together with the Delft University of Technology. It aims to build a community of scientists in hydrology who use different programming languages for their specific models. Python is the lingua franca of the eWaterCycle platform and requires no modification to a particular model, making the platform user-friendly and flexible. Therefore, it can readily be applied in other geoscientific models. Currently, the Python data assimilation package includes ensemble-type methods, particle filters, and their variants, which are all sequential data assimilation algorithms. The implementation of techniques related to localization and inflation methods is included in this package. Localization and inflation are effective ways to avoid the collapse of a filter, which happens commonly in high dimensional models. The package gives access to all tunable parameters by configuration files quickly. To evaluate the performance of data assimilation comprehensively, a series of metrics is provided. In addition, the package offers a set of visualization tools to explore the results of data assimilation and the improvement of models.&amp;lt;/p&amp;gt;