Reviewerly/exHarmony huggingface dataset:
Application of Recommendation Medical Specialty Doctors Based on User Symptoms Using the C4.5 Method and K-Nearest Neighbor

Abstract This paper is about web based application of recommendation medical specialty doctors based on user symptoms using the C4.5 method and K-Nearest Neighbor. Classification is the process of grouping things based on classes with the characteristics of similarities and differences. This objects or entities are labeled as user symptoms. User symptoms are classified and produce a category of doctor that matches the user’s symptoms. This application will be using sample data from ascertain the opinions and experience of Doctor Yohannes Cahyadi who practices at the Griya Kasih Indah Clinic and from the website alodokter, hellosehat, docdoc.com, cicendoeyehospital, and the eye clinic nusantara. The purpose of making this application is to choose the right specialist doctor based on human symptoms or medical need. The test results obtained said that the best classification accuracy is to use the K-Nearest Neighbor method with an accuracy value of 100%, this shows that the use of the C4.5 method and K-Nearest Neighbor can provide recommendations for the right doctor with user symptoms.
Professional-oriented practical training - a factor in the development of integration of education and production

The positive results of improving the educational process in higher technical educational institutions on the basis of professional orientation, taking into account the requirements and needs of customer enterprises, are considered.
Evaluation of Facebook advertising recommendations explanations with the perspective of semiotic engineering

Recommendation systems (SRs) have a major impact on what people consume daily. In order to achieve the goal of recommending relevant items, in addition to improving the accuracy of the recommendation algorithms, it is necessary to use user-centered approaches, so that SRs can inspire trust and loyalty, increasing users satisfaction, and convincing them to recommended item. One way to achieve these objectives is to explain the recommendations generated by these systems. An explanation starts from a communicative process between a sender and a receiver. Thus, in addition to user-centered approaches, it is also necessary to think of a communication-centered approach. In this work, we propose to explore and reflect, from a Semiotic Engineering perspective, on how the recommendation systems explanation process happens. Thus, we carried out an evaluation of the quality of the explanation given by Facebook regarding the advertising recommendations offered in the users timeline, based on the Semiotic Inspection Method and the application of a questionnaire.
Reversible Color Image Watermarking Algorithm using Reverse Contrast Mapping

A reversible digital color image (RGB) watermarking algorithm based upon reversible contrast mapping (RCM) is being discussed in this paper. RCM is a computationally efficient spatial technique that manipulates the pairs of pixels with easy integer mathematics. Unlike other methods such as difference expansion, histogram modification, prediction error expansion along with adaptive pairing etc. and their various modified versions which considers context of a pixel. Involving more pixels may lead to much computational complexity and time. This method doesn’t need a location map to be stored and saves space. At the embedding stage, the color image is divided into three color intensity planes and watermark information can be embedded into each of the three planes. At the decoding stage, obtain the watermark by collecting bits from three (RGB) components. Net amount of data that can be embedded into color image is increased as compared to grayscale images of same size. Hence proposed algorithm gives good embedding capacity(EC). The experimental results show the values of embedding capacity and two quality metrics PSNR & SSIM for various standard test images. The proposed scheme does not need any location map to embed and hence no need of data compression.
An analysis of pre-service primary school English teachers’ perceptions on online asynchronous English learning

The purpose of this study is to examine the effect of online-based non face-to-face learning for university level students of teacher education, comparing it with the traditional face-to-face one. For this research goal, the researchers targeted and surveyed 90 students of a national university of education in order to investigate their perceptions on the online-based non-contact approach and to find out whether or not there is any difference between genders, year levels, and majors. To fully and better understand the students’ perceptions and opinions on the non face-to-face instruction, a student-focus group of 8 pre-service teachers has been created for collecting their voices. Both quantitative and qualitative methods are used together in this study to solidify the reliability of the data analysis. The results of this study are summarized as follows: First, there was not a statistically significant difference between the traditional face-to-face instruction and the online-based non face-to-face one. Second, a statistically significant difference was found not in both year-levels and majors but in genders. Third, the participants by and large were satisfied with the effect of the online-based non face-to-face instruction but they clearly pointed out that the most serious problem of this non traditional approach results from the lack of sufficient interactions between teachers and students.
Geometric robust descriptor for 3D point cloud.

We propose rotation robust and density robust local geometric descriptor. Local geometric feature of point cloud is used in many applications, for example, to find correspondences in 3D registration and to segment local regions. Usually, application accuracy depends on the discriminative power of the local geometric features. However, there are some problems such as point sparsity, rotated point cloud, and so on. In this paper, we present new local feature generation method to make a rotation robust and density robust descriptor. First, we place kernels aligned around each point and align them to the normal of the point. To avoid the sign problem of the normal vector, we use symmetric kernel point distribution with respect to the tangent plane. Next, from each kernel point, we estimate geometric information which is rotation robust and discriminative. Finally, we operate convolution process with consideration of kernel point structure, and aggregate all kernel features. We experiment our local descriptors on the ModelNet40 dataset for registration and classification, and the ShapeNet part dataset for segmentation. Our descriptor shows discriminative power regardless of point distribution.
Are Cryptos Safe-Haven Assets during COVID-19? Evidence from Wavelet Coherence Analysis

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Active methods of teaching technical disciplines in educational organizations

Abstract The article discusses the possibility of improving the quality of vocational training and the readiness of graduates of higher educational institutions for professional activity through the use of active teaching methods in the educational process in the study of technical disciplines for the formation of general cultural, general professional and professional competencies.
A Novel Probabilistic Neural Network with Unlearned-class Detection Based on Normal and Complementary Gaussian Mixture Models

General classification methods only involve consideration of learned classes, and do not cover undefined targets such as unintended characteristics in the learning process. This paper proposes a novel probabilistic neural network can treat unlearned class. The proposed method incorporates two types of probabilistic distribution: normal and complementary Gaussian distribution and can reach multi-class classification and unlearned class detection with a single network. In the experiments, artificial data and electromyogram (EMG) patterns were classified to demonstrate the capabilities of the proposed method. The results showed that the approach produces high performance for classification, and there were significant differences between the proposed and previous methods.
Digital educational platform as a personnel management tool

Abstract External and internal challenges facing the domestic system of vocational education today require structural and managerial changes in professional educational organizations that facilitate the adoption of effective timely management decisions and, at the same time, create conditions for the emergence of initiatives focused on the sustainable development of an educational organization including those related to the professional activities of teachers. The article deals with the management system of the educational process of vocational education from the classical theory of automatic control point of view. The structure of combined management, allowing to implement the management process of the educational organization of vocational secondary education, based on the process management model of the “Deming cycle” principle is proposed. The input and output parameters, control and setting actions are concretized. The implementation of control systems based on subsystems for monitoring the effectiveness of educational and methodological support activities and management, integrated into the digital educational platform of secondary vocational education, is proposed.
Continuous Time Bayesian Networks with Clocks

Structured stochastic processes evolving in continuous time present a widely adopted framework to model phenomena occurring in nature and engineering. However, such models are often chosen to satisfy the Markov property to maintain tractability. One of the more popular of such memoryless models are Continuous Time Bayesian Networks (CTBNs). In this work, we lift its restriction to exponential survival times to arbitrary distributions. Current extensions achieve this via auxiliary states, which hinder tractability. To avoid that, we introduce a set of node-wise clocks to construct a collection of graph-coupled semi-Markov chains. We provide algorithms for parameter and structure inference, which make use of local dependencies and conduct experiments on synthetic data and a data-set generated through a benchmark tool for gene regulatory networks. In doing so, we point out advantages compared to current CTBN extensions.
Fluorescent Mineral Photography: A Shot In The Dark

All photos by Jordan Root of specimens in his collection, except as notedFluorescent minerals are both uniquely beautiful and, in my opinion, vastly underappreciated. One factor hindering the appre...
Long-Range Interaction Enhanced Adiabatic Quantum Computers

A quantum computer is not necessarily alone, e.g., thousands and millions of quantum computers are simultaneously working together for adiabatic quantum computers based on nuclear spins. Long-range interaction is inevitable between these nuclear spin qubits. Here we investigate the effect of long-range dipolar interaction between different adiabatic quantum computers. Our analytical and numerical results show that the dipolar interaction can enhance the final fidelity in adiabatic quantum computation for solving the factorization problem, when the overall interaction is negative. The enhancement will become more prominent if a single quantum computer encounters an extremely small energy gap which occurs more likely for larger-size systems.
Analysis of Vector Space Method in Information Retrieval for Smart Answering System

In the world of internet, searching play a vital role to retrieve the relevant answers for the user specific queries. The most promising application of natural language processing and information retrieval system is Question answering system which provides directly the accurate answer instead of set of documents. The main objective of information retrieval is to retrieve relevant document from a huge volume of data sets underlying in the internet using appropriatemodel. There are many models proposed for retrieval process such as Boolean, Vector space and Probabilistic method. Vector space model is best method in information retrieval for document ranking with efficient document representation which combines simplicity and clarity. VSM adopts similarity function to measure the matching between documents and user intent, and assign scores from the biggest to smallest. The documents and query are assigned with weights using term frequency and inverse document frequency method. To retrieve most relevant document to the user query term, document ranking function cosine similarity score is applied for every document and user query. The documents having more similarity scores will be considered as relevant documents to the query term and they are ranked based on these scores. This paper emphasizes on different techniques of information retrieval and Vector Space Model offers a realistic compromise in IR processing. It allows best weighing scheme which ranks the set of documents in order of relevance based on user query.
Improving NMT via Filtered Back Translation

Document-Level Machine Translation (MT) has become an active research area among the NLP community in recent years. Unlike sentence-level MT, which translates the sentences independently, document-level MT aims to utilize contextual information while translating a given source sentence. This paper demonstrates our submission (Team ID - DEEPNLP) to the Document-Level Translation task organized by WAT 2020. This task focuses on translating texts from a business dialog corpus while optionally utilizing the context present in the dialog. In our proposed approach, we utilize publicly available parallel corpus from different domains to train an open domain base NMT model. We then use monolingual target data to create filtered pseudo parallel data and employ Back-Translation to fine-tune the base model. This is further followed by fine-tuning on the domain-specific corpus. We also ensemble various models to improvise the translation performance. Our best models achieve a BLEU score of 26.59 and 22.83 in an unconstrained setting and 15.10 and 10.91 in the constrained settings for En->Ja & Ja->En direction, respectively.
OFDM Receiver Using Deep Learning: Redundancy Issues

To combat the inter-symbol interference (ISI) and the inter-block interference (IBI) caused by multi-path fading in orthogonal frequency-division multiplexing (OFDM) systems, it is usually recommended employing a cyclic prefix (CP) with length equal to the channel order. In some practical cases, however, the channel order is not exactly known. Looking for a balance between a full-sized CP and its absence, we investigate the redundancy issues and propose a minimum redundancy OFDM receiver using deep-learning (DL) tools. In this way, we can benefit from an improved reception performance, when compared with CP-free case, and also a better spectrum utilization when compared with the CP-OFDM case. Moreover, compared with the CP-free case, improved performance can be obtained even when the channel order is not available. Simulation results indicate that a good BER level can be achieved and the proposed technique can also be applied in other DL-based receivers.
A Self-adaptive Multi-hierarchical Modular Neural Network for Complex Problems

Due to the fact that the number of function models and the structure of the sub-model of the modular neural network are difficult to determine when applied to complex problems. This paper presents a self-adaptive multi-hierarchical modular neural network structure design method. In this method, a fast find of density peaks cluster algorithm is adopted to determine the number of the function modules, and a conditional fuzzy clustering algorithm is used to further divide the training samples of each function module into several groups to determine the number of sub-modules in each function module. For each sub-module, an incremental design of radical basis function (RBF) network network algorithm based on train error peak is applied to construct the structure of sub-modules which can self-adaptively build the structure of the sub-modules based on the training samples that allocated to the sub-modules. In sub-modules integration, a sub-module integrate approach based on relative distance measure is applied which can select different sub-modules from different function modules to collaboratively learning the training samples. Experiment results demonstrate that the self-adaptive multi-hierarchical modular neural network can not only solve the complex problems that the fully coupled RBF difficult to deal with, but also can improve the learning accuracy and generalization performance of the network.
Constructive truth and falsity in Peano arithmetic

Abstract Artemov (2019, The provability of consistency) offered the notion of constructive truth and falsity of arithmetical sentences in the spirit of Brouwer–Heyting–Kolmogorov semantics and its formalization, the logic of proofs. In this paper, we provide a complete description of constructive truth and falsity for Friedman’s constant fragment of Peano arithmetic. For this purpose, we generalize the constructive falsity to $n$-constructive falsity in Peano arithmetic where $n$ is any positive natural number. Based on this generalization, we also analyse the logical status of well-known Gödelean sentences: consistency assertions for extensions of PA, the local reflection principles, the ‘constructive’ liar sentences and Rosser sentences. Finally, we discuss ‘extremely’ independent sentences in the sense that they are classically true but neither constructively true nor $n$-constructively false for any $n$.
Wildcard error: Quantifying unmodeled errors in quantum processors

Error models for quantum computing processors describe their deviation from ideal behavior and predict the consequences in applications. But those processors' experimental behavior -- the observed outcome statistics of quantum circuits -- are rarely consistent with error models, even in characterization experiments like randomized benchmarking (RB) or gate set tomography (GST), where the error model was specifically extracted from the data in question. We show how to resolve these inconsistencies, and quantify the rate of unmodeled errors, by augmenting error models with a parameterized wildcard error model. Adding wildcard error to an error model relaxes and weakens its predictions in a controlled way. The amount of wildcard error required to restore consistency with data quantifies how much unmodeled error was observed, in a way that facilitates direct comparison to standard gate error rates. Using both simulated and experimental data, we show how to use wildcard error to reconcile error models derived from RB and GST experiments with inconsistent data, to capture non-Markovianity, and to quantify all of a processor's observed error.
Weighted Reed–Solomon convolutional codes

In this paper we present a concrete algebraic construction of a novel class of convolutional codes. These codes are built upon generalized Vandermonde matrices and therefore can be seen as a natural extension of Reed–Solomon block codes to the context of convolutional codes. For this reason we call them weighted Reed–Solomon (WRS) convolutional codes. We show that under some constraints on the defining parameters these codes are Maximum Distance Profile (MDP), which means that they have the maximal possible growth in their column distance profile. We study the size of the field needed to obtain WRS convolutional codes which are MDP and compare it with the existing general constructions of MDP convolutional codes in the literature, showing that in many cases WRS convolutional codes require significantly smaller fields.
Aspects of Visual Communication Design in Animated Learning Media for Early Childhood and Kindergarten

Visual communication design media through animation is the most appropriate means of assisting learning methods in early childhood which functions to help early childhood develop sensory and motor skills in early childhood. Also, it is constructive for teachers and parents to facilitate teaching methods that can concretely stimulate motivation in students. This study will describe a learning model that uses visual communication designs, especially animation in early childhood in the Surakarta area, Central Java, Indonesia. This research method includes data collection, data analysis, and presentation of data analysis. The stages of analysis from various sources of results from information from both literature reviews and interviews were carried out using an interactive model, where each component of the research includes the stages of data collection, data reduction, presentation and conclusion drawing. Test the validity of the data using data triangulation techniques, namely triangulation of data sources and triangulation of theories. The data analysis technique uses the stages of data collection, data reduction, data presentation, and concluding.
NRA Media and Second Amendment Identity Politics

This chapter considers the National Rifle Association (NRA) as not merely a lobbying outfit, trade association, or hobbyist group, but as a full-fledged mediasphere. Since the early 2000s, the NRA has aggressively expanded its footprint within the broader right-wing media environment—it publishes four print magazines and a highly integrated array of micro-targeted online print and video content, social media platforms, and original online television programming. Via a content analysis of NRA.org, a site that aggregates and prioritizes content from across the group’s multimedia platforms, this chapter employs critical discourse analysis to illuminate the site’s populist themes and rhetorical styles. It finds that the NRA combines the trappings of news genres and right-wing discourses with populist modes of expression to amplify and reinforce the deep affective ties between gun ownership and conservative political identity.
The AI Accident Network: Artificial Intelligence Liability Meets Network Theory

Network theory holds great unappreciated value when it comes to analyzing and confronting new legal issues induced by emerging technologies. It can assist regulators, judges and insurers to visualize multifaceted legal questions in a manner that assists them to better understand the complexity of new legal enquiries, and the appropriate policy intervention. A complex challenge the legal realm faces today is that of Artificial Intelligence (AI) liability. This Article will utilize network theory in this context. It will do so by combining network theory's features with Fletcher's nonreciprocal risks paradigm. This combination will provide essential tools which will show that a strict liability regime is the best-suited regime to apply when AI causes harm, and will provide indicators to identify the entity who should be held strictly liable.Fletcher's nonreciprocal paradigm was heavily criticized upon its emergence. Calabresi's cheapest-cost avoider strict liability approach eventually became the prominent approach in the context of strict liability. However, this Article claims that today's network economy and networked society offers a new opportunity for Fletcher's paradigm to be applied given the new ways in which we interact and inflict damages upon each other, which were not feasible in the past. This Article integrates network theory into the field of AI tort law for the first time. It presents a new and unique methodology to the ongoing dispute about the appropriate liability regime that should apply when AI causes damages.