Reviewerly/exHarmony huggingface dataset:
Website Based Application of Doctor Selection Classification Derive From Patient Complaints Using the C4.5 Method and K-Nearest Neighbor

Abstract This paper is about web based application of doctor selection classification, derive from patient complaints by utilizing 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 complaints. User complaints are classified and produce a category of doctor that matches the user’s complaints. 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 a doctor selection classification application based on patient complaints is so that the user can find out the doctor’s category recommendations in accordance with user complaints input on this application. 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 complaints.
Mind Mapping on Students’ Writing Descriptive Text

This research focused on students’ writing descriptive text by using mind mapping strategy. The research was experimental research that uses one group pre-test and post-test design with 32 students in experimental and control class at Grade VIII Students of SMP N 6 Padangsidimpuan. The data were collected from students’ writing test that consists of 32 students for experimental class and 32 students also for control class. The result of the research have found from t-test, which is t-count higher than t-table 6.22 ˃ 1.66. It was concluded that mind mapping strategy has a significant effect on the writing descriptive text of students.
Analysis on the Innovative Ways of Employment and Entrepreneurship Guidance in Higher Vocational Colleges Under the Education of "Three All-round Education"

The educational concept of "Three All-round Education" emphasizes all-staff education, full-process education, and all-round education. At present, this education concept has been applied in many courses and is a new era curriculum, one of the important ideological guidelines for teaching reform. As far as the employment and entrepreneurship guidance work and teaching in higher vocational colleges are concerned, this is an important guiding course for solving the employment and entrepreneurship problems of higher vocational graduates in the new era. The quality of course teaching must be ensured and guided by the concept of "three comprehensive education". It is of great significance for curriculum innovation and development. This article introduces the basic connotation of the "Three Comprehensive Education", analyzes the necessity of the application of the "Three Comprehensive Education" concept in the employment and entrepreneurship guidance work of higher vocational colleges, and explores the actual situation of the employment and entrepreneurship guidance work in the higher vocational colleges. This is the innovation path of employment and entrepreneurship guidance work in higher vocational colleges under the education of "Three All-round Education".
Designing and Testing of a Semi-Physical QUIC Video Streaming Simulation Platform

QUIC (Quick UDP Internet Connections) is a streaming control protocol that proposed by Google in recent years.In order to study the ability of QUIC to support streaming video in a wireless mobile environment, we combine a DASH (Dynamic Adaptive Streaming HTTP) server and QUIC source code to design and implement a virtual machine-based testing and simulation system.The system can not only use real video traffic for streaming video playback, but also use the simulation system such as NS3 to introduce wireless network features.Based on the system, we perform a simulation analysis on the proposed QUIC stream video improvement strategy for large-delay ACK discarding (LDAD).The results demonstrate that the simulation platform can help to understand and investigate the interaction between streaming video and QUIC in-depth.
Towards Boosting the Channel Attention in Real Image Denoising : Sub-band Pyramid Attention

Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally without feature selection flexibility. While using ANNs for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the performance. Channel attention methods in real image denoising tasks exploit dependencies between the feature channels, hence being a frequency component filtering mechanism. Existing channel attention modules typically use global statics as descriptors to learn the inter-channel correlations. This method deems inefficient at learning representative coefficients for re-scaling the channels in frequency level. This paper proposes a novel Sub-band Pyramid Attention (SPA) based on wavelet sub-band pyramid to recalibrate the frequency components of the extracted features in a more fine-grained fashion. We equip the SPA blocks on a network designed for real image denoising. Experimental results show that the proposed method achieves a remarkable improvement than the benchmark naive channel attention block. Furthermore, our results show how the pyramid level affects the performance of the SPA blocks and exhibits favorable generalization capability for the SPA blocks.
COMPUTER VIRUSES AND VIRUS PROTECTION PROBLEMS

Computer viruses are the most pressing problem of many today. Computer virus remains one of the reasons for the loss of information and the main thing. It is known that viruses have led many organizations and companies to disrupt their work. A computer virus is a specially written program that can be attached to other programs (that is, it performs its performance), as well as perform unwanted actions on the computer. The Virus does not always carry out actions to infect the software in order to hide itself. They are realized only under certain conditions. The fact is that viruses use all sorts of ways to get rid of attachments to their systems, so there are no systems that do not get infected. Computer viruses, along with their characteristics, working, effects on the computer systems and to suggest measures for detecting the virus infection in a computer system and to elaborate means of prevention.
Hybrid Teaching: Teaching-Learning Process by Technological Tools, Challenges and Possibilities

Much is discussed about the effective use of electronic devices along the internet to develop educational practices in school environments, as well as, extend this action to create a relationship network for the external area and establishing a practical and productive communication to build this process of such singularity.and importance in the lives of so many people.In this scenario, it is extremely important to understand the challenges and possibilities that Hybrid Teaching in its implementation can contribute to the development of students' integral learning .In this context, this study has the general objective of reflecting and researching the challenges of contemporary education ,comprises ing the dynamics that the hybrid teaching involves the teachers and student demonstrations as a nonlinear and highly active process.This study becomes considerable since it portrays a highly considerable level of relevance, consequently generating benefits for a promising educational future and its pedagogical aspects.However, the study was supported by a bibliographic research , which is based on references published in magazines, periodicals, books, allowing to establish a sum in order to enrich the research , which , in turn, is a way for researchers to interact about what has already been studied .This research presented a call for great reflections and a healthy debate about the great results that can emerge with the implementation of hybrid education.Theories committed to the radical change that education needs, the challenges imposed and possible to be overcome and the need for partnership with the technologies.
Intercept-resend attack on passive side channel of the light source in BB84 decoy-state protocol

Abstract Quantum key distribution (QKD) promises unconditionally secure communication based on fundamental laws of physics, though practical realizations of QKD may have loopholes and side channels, thus their security can be compromised. We analyze the intercept-resend attack on the BB84 decoy-state protocol under the presence of a passive side channel of the light source. We derive an upper bound for the secret key rate and show that it does not significantly deviate from the ideal side-channel-free case.
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes.

In this paper, we aim at learning the relationships and similarities of a variety of tasks, such as humour detection, sarcasm detection, offensive content detection, motivational content detection and sentiment analysis on a somewhat complicated form of information, i.e., memes. We propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. For multi-tasking, we propose two attention-like mechanisms viz., Inter-task Relationship Module (iTRM) and Inter-class Relationship Module (iCRM). The main motivation of iTRM is to learn the relationship between the tasks to realize how they help each other. In contrast, iCRM develops relations between the different classes of tasks. Finally, representations from both the attentions are concatenated and shared across the five tasks (i.e., humour, sarcasm, offensive, motivational, and sentiment) for multi-tasking. We use the recently released dataset in the Memotion Analysis task @ SemEval 2020, which consists of memes annotated for the classes as mentioned above. Empirical results on Memotion dataset show the efficacy of our proposed approach over the existing state-of-the-art systems (Baseline and SemEval 2020 winner). The evaluation also indicates that the proposed multi-task framework yields better performance over the single-task learning.
Fast marching based superpixels

Abstract In this article, we present a fast-marching based algorithm for generating superpixel (FMS) partitions of images. The idea behind the algorithm is to draw an analogy between waves propagating in a heterogeneous medium and regions growing on an image at a rate depending on the local color and texture. The FMS algorithm is evaluated on the Berkeley Segmentation Dataset 500. It yields results in terms of boundary adherence that are slightly better than the ones obtained with similar approaches including the Simple Linear Iterative Clustering, the Eikonal-based region growing for efficient clustering and the Iterative Spanning Forest framework for superpixel segmentation algorithms. An interesting feature of the proposed algorithm is that it can take into account texture information to compute the superpixel partition. We illustrate the interest of adding texture information on a specific set of images obtained by recombining textures patches extracted from images representing stripes, originally constructed by Giraud et al. [20]. On this dataset, our approach works significantly better than color based superpixel algorithms.
Asymptotics of an empirical bridge of regression on induced order statistics

We propose a class of tests for linear regression on concomitants (induced order statistics). These tests are based on sequential sums of regression residuals. We self-center and self-normalize these sums. The resulting process is called an empirical bridge. We prove weak convergence of the empirical bridge in uniform metrics to a centered Gaussian process. The proposed tests are of chi-square type.
Identification of Information Security Controls for Fitness Wearable Manufacturers

Statista suggests that there would be 368.2 million wearables shipped globally in 2020 with a projection of 500 million by 2024. These predictions are becoming a reality considering the fast growing of Intent of Things (IoT) domain. These wearables come in different forms, shapes, and sizes. The existence of fitness wearables encourages people to participate in a healthy lifestyle through tracking of health and fitness-related activities. The functionality of these devices includes gathering, processing, transmitting, and storing user data. However, these devices carry with them vulnerabilities that can negatively affect the security and privacy of a user. Therefore, the primary objective of this study is to identify security controls to mitigate the vulnerabilities that affect fitness wearables from a security and privacy perspective. However, to identify these security controls, the researcher firstly identifies the vulnerabilities affecting these fitness wearables. This study executed a methodology in two stages. The first stage conducted a literature review to identify the vulnerabilities affecting fitness wearables and related components within the ecosystem of fitness wearables. The second state follows a systematic analysis approach to identify security controls for the fitness wearable manufacturers to mitigate these vulnerabilities. The final output of this study indicates the security complexities surrounding the fitness wearables by presenting the study limitations.
Optimal Labeling with Cyclic Gray Codes for Generalized 16PAM Constellations

The mutual information achieved by a pulse-amplitude modulated constellation depends strongly on the symbol labels. In general, these symbols are labeled based on a Gray code mapping, in which neighboring label bit-strings differ by only one bit value. For a 16-symbol constellation, there are numerous such Gray code mappings satisfying this property, each with distinct mutual information performance over SNR. In this paper, we present an investigation of the optimal labeling using cyclic Gray codes for symmetric 16-symbol one-dimensional constellations with uniform symbol probability and non-uniform spacing. These results can be extended to 256-symbol quadrature amplitude modulated constellations with in-phase/quadrature symmetry.
Alignment of Senior High School Strand in College Course

Through the implementation of Republic Act (RA) 10533 or K-12 Program during the Aquino III administration an additional 2 years in High School, called Senior High School, was added to the curriculum. The researchers used structured questionnaire and Google form to properly disseminate the questions. The result of the gathered data showed that 80.67% of the strands of the 119 pioneer SHS graduates of Bulacan State University Laboratory High School are aligned to their current course. The researchers also determined that (a) Strong Desire/Interest in the Course, (b) Possible Financial Outcome, and (c) Employment Prospects were the 3 major factors that contributed to their decision in enrolling in their current course. The researchers concluded that the respondents chose their course according to their interest in their future profession and future financial and employment matters in connection to their previous Senior High School strands. The researchers recommend this research to be conducted with the succeeding batch of Senior High School of Bulacan State University Laboratory High School to determine if the administrators and coordinators continuously do the mandatory guides and seminars for their students and to determine whether the students planned beforehand for their future.
Developing Android Action Game “I am” Using Gyroscope and Augmented Reality

Abstract “I am” is an action game with Gyroscope to directing player according to smartphone’s orientation and Augmented Reality to scan markers to obtain skills and items. “I am” is made with Unity Game Engine, C# to program, Adobe Photoshop and Illustrator to design User Interface and buttons. “I am” tells the story of a witch named Rogana which lost his memories with severe wounds. Rogana was defeated by Karan who can leap through dimension as he wish to. Karan has the ambition to become the strongest magician. Rogana survived and tried to take revenge on Karan. Testing on this game done by blackbox, alpha testing and beta testing through a questionnaire conducted by 31 people. Test results show that the game “I am” has a unique gameplay with Gyroscope and Augmented Reality features.
Type-Based Declassification for Free

This work provides a study to demonstrate the potential of using off-the-shelf programming languages and their theories to build sound language-based-security tools. Our study focuses on information flow security encompassing declassification policies that allow us to express flexible security policies needed for practical requirements. We translate security policies, with declassification, into an interface for which an unmodified standard typechecker can be applied to a source program—if the program typechecks, it provably satisfies the policy. Our proof reduces security soundness—with declassification—to the mathematical foundation of data abstraction, Reynolds’ abstraction theorem.
JAXNS: a high-performance nested sampling package based on JAX

Since its debut by John Skilling in 2004, nested sampling has proven a valuable tool to the scientist, providing hypothesis evidence calculations and parameter inference for complicated posterior distributions, particularly in the field of astronomy. Due to its computational complexity and long-running nature, in the past, nested sampling has been reserved for offline-type Bayesian inference, leaving tools such as variational inference and MCMC for online-type, time-constrained, Bayesian computations. These tools do not easily handle complicated multi-modal posteriors, discrete random variables, and posteriors lacking gradients, nor do they enable practical calculations of the Bayesian evidence. An opening thus remains for a high-performance out-of-the-box nested sampling package that can close the gap in computational time, and let nested sampling become common place in the data science toolbox. We present JAX-based nested sampling (JAXNS), a high-performance nested sampling package written in XLA-primitives using JAX, and show that it is several orders of magnitude faster than the currently available nested sampling implementations of PolyChord, MultiNEST, and dynesty, while maintaining the same accuracy of evidence calculation. The JAXNS package is publically available at \url{this https URL}.
Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks.

Pre-training large-scale language models (LMs) requires huge amounts of text corpora. LMs for English enjoy ever growing corpora of diverse language resources. However, less resourced lan- guages and their mono- and multilingual LMs often struggle to ob- tain bigger datasets. A typical approach in this case implies using machine translation of English corpora to a target language. In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness. In the empirical evaluation, we perform a comparison of directly translated against curated Spanish SQuAD datasets on both user and system levels. Further experimental results on XQuAD and MLQA down- stream transfer-learning question answering tasks show that presum- ably multilingual LMs exhibit more resilience to machine translation artifacts in terms of the exact match score.
Multi-modal human-computer interaction system in cockpit

Abstract In order to explore new human-machine interaction methods, a set of multi-modal human-machine interaction coordinated control system is proposed, which realizes the basic flight control based on the change of the pilot’s field of view, touch control, voice control and other information obtained from multi-mode coordinated control. This system introduces a new type of human-computer interaction into the cockpit application on the basis of the human-computer interaction interface for flight driving, and has carried out research on multi-mode collaborative control system, mainly including eye movement interaction, touch interaction gesture Interaction and voice interaction. Finally, this project formed a multi-mode cooperative control software and hardware system.
A multimodal analysis of digitally mediated service encounters using ‘super-sticky’ applications: Implications from the micro to the macro

Abstract Digitally mediated service encounters such as digital payments are becoming more common and super-sticky social media such as WeChat, applications which mediate an indispensible part of our day-to-day practices, are increasingly used as the main tool for these services. However, how digital technology affects the way semiotic modes realize lower level actions in service encounters has not widely been studied. This paper carried out a multimodal analysis of videoed service encounters combined with interviews and ethnographic field notes to analyze how the use of WeChat Pay affects how modes such as proxemics, gaze and gesture realize lower level actions such as handing, smartphone handling, and ratifying payments. Second, analyses of the wider semiotic trajectories involved in the use of super-sticky social media in digitally mediated service encounters show how a mundane digital payment has important implications for how we record and disclose our personal behavior in big data and for the distribution of knowledge and skills deploying semiotic resources and digital literacies in society.
Paradoxes of probabilistic programming

Abstract Probabilistic programming languages allow programmers to write down conditional probability distributions that represent statistical and machine learning models as programs that use observe statements. These programs are run by accumulating likelihood at each observe statement, and using the likelihood to steer random choices and weigh results with inference algorithms such as importance sampling or MCMC. We argue that naive likelihood accumulation does not give desirable semantics and leads to paradoxes when an observe statement is used to condition on a measure-zero event, particularly when the observe statement is executed conditionally on random data. We show that the paradoxes disappear if we explicitly model measure-zero events as a limit of positive measure events, and that we can execute these type of probabilistic programs by accumulating infinitesimal probabilities rather than probability densities. We believe that our extension improves probabilistic programming languages as an executable notation for probability distributions by making it more well-behaved and more expressive, at the cost of the programmer having to be explicit about which limit is intended when conditioning on an event of measure zero.