Reviewerly/exHarmony huggingface dataset:
Sosialisasi Penggunaan Media Pembelajaran Berbasis IT untuk Menanggulangi Dampak Covid-19 dalam Bidang Pendidikan

Information technology today is very much needed by every society, especially during the current pandemic. The development of information technology which is increasingly rapid in the current era of globalization cannot be avoided anymore its impact on the world of education. Global demands call upon the world of education to always and constantly adapt technological developments to the steps in improving the quality of education, especially adjusting its use for the world of education, especially in the learning process. There are many learning media technologies in the form of platforms that are used in every educational institution, at the school level to streamline the learning process carried out, such as classrooms, google forms, quizizz and zoom. PLSP Group of the Indonesian Education University, Purwakarta Campus, endeavors to solve problems through extension programs and instructional media training for teachers. The goal is that the skills of teachers in making teaching materials using learning media based on information technology and the internet can be improved.
Alternative Constructions of Asymmetric Primitives from Obfuscation: Hierarchical IBE, Predicate Encryption, and More

We revisit constructions of asymmetric primitives from obfuscation and give simpler alternatives. We consider public-key encryption, (hierarchical) identity-based encryption ((H)IBE), and predicate encryption. Obfuscation has already been shown to imply PKE by Sahaiand Waters (STOC’14) and full-fledged functional encryption by Garget al. (FOCS’13). We simplify all these constructions and reduce the necessary assumptions on the class of circuits that the obfuscator needs to support. Our PKE scheme relies on just a PRG and does not need any puncturing. Our IBE and bounded HIBE schemes convert naturalkey-delegation mechanisms from (recursive) applications of puncturable PRFs to IBE and HIBE schemes. Our most technical contribution is an unbounded HIBE, which uses (public-coin) differing-inputs obfuscationfor circuits and whose proof relies on a recent pebbling-based hybrid argument by Fuchsbauer et al. (ASIACRYPT’14). All our constructions are anonymous, support arbitrary inputs, and have compact keys and ciphertexts
KIDNEY STONE DETECTION USING IMAGE ENHANCEMENT TECHNIQUES

A kidney stone termed as renal calculi is a solid piece of material that forms in a kidney when substances that are normally found in the urine become highly concentrated. To detect these kidney stones from the X-ray image we reduce the noise by filtering the image and adjust the contrast of the image by using histogram equalization where it spreads out the most frequent pixel intensity values or stretches out the intensity range of the image. The processed image undergoes thresholding level segmentation followed by segmentation using watershed Algorithm. The implementation of proposed technique is done using open CV and python. The image obtained as a result of watershed algorithm undergoes Contour Segmentation where it iterates through each contour and computes the Bounding Rectangle followed by approximate Poly DP contour technique which approximates a contour shape to another shape with less number of vertices depending upon the precision we specify. Lastly gamma transformation is applied, where overall brightness of an image is controlled by expanding the range of intensity levels in an image so that it spans the full intensity of the captured image. As a result, it has been determined that the proposed approach gives fast, accurate and reliable results with around 90 percent accuracy for classification and about 6 percent error in stone detection targets.
KEMAMPUAN MENELAAH STRUKTUR DAN KAIDAH KEBAHASAAN TEKS BERITA YANG DIBACA SISWA KELAS VIII MTS MATHLA’UL ANWAR CINTAMULYA CANDIPURO LAMPUNG SELATAN TAHUN PELAJARAN 2018/2019

The problems in this thesis are based on the difficulties and weaknesses of the eighth grade students of the even semester MTs Mathla'ul Anwar Cintamulya Candipuro in reviewing the structure and rules of language of the read text. The aspects studied include the news text structure: title, date line, news terrace, news text coupler, news content, closing news and language rules: diction, sentence, and spelling. The method uses quantitative descriptive. The population of this research is the odd grade VIII semester of MTs. Mathla'ul Anwar which amounted to 73 with a sample class VIII.1 which amounted to 23 students. The sampling technique uses cluster random sampling.Data collection techniques used assignments and the results were analyzed by quantitative analysis. The results showed that students were quite capable in analyzing the structure and rules of language of the news text that read the average score achieved was 68. In each aspect, the results showed excellent ability in the news text structure, news headlines, dates, and terraces. news with an average score of 100, while in the news chain aspect it achieves an average score of 70 in sufficient categories. Furthermore, on the indicator of content all students achieve an average score of 85 in good categories while in the aspect of news legs students achieve an average score of 77 in the good category. In the aspects of the language rules section of the news text, indicators of diction of students obtain an average score of 33 in the less category and in the aspect of the news sentence students achieve an average score of 26 in the less category. Meanwhile, in the aspect of spelling students achieved an average score of 41 in the less category.
JSAnalyzer: A Web Developer Tool for Simplifying Mobile Pages Through JavaScript Optimizations

The amount of JavaScript embedded in Web pages has substantially grown in the past decade, leading to large and complex pages that are computationally intensive for mobile devices. In this paper, we propose JSAnalyzer, an easy-to-use tool that enables Web developers to quickly optimize and generate simpler versions of existing web pages for mobile users. JSAnalyzer can selectively enable or disable JavaScript elements in a page while visually observing their impact, such that non-critical elements can be removed without sacrificing the visual content or the interactive functionality. Our quantitative evaluation results show that JSAnalyzer achieves more than 88% relative increase in performance scoring for low-end mobile phones (i.e., from 32% to 60%), and reduces the page load time by 30%. A qualitative study of 22 users shows that JSAnalyzer maintains more than 90% visual similarity to the original pages, whereas a developer evaluation study conducted with 23 developers shows that JSAnalyzer scores more than 80% in terms of usefulness and usability while retaining the page content and functional features. Additionally, we show that JSAnalyzer outperforms state-of-the-art solutions such as JSCleaner and Google AMP.
ESSENTIAL SIGNS OF THE IMPLEMENTATION OF THE ASCENDING FORM OF DEVELOPMENT OF THE “ABSTRACT” IN THE FINE ARTS

The article is devoted to the analysis of the method of “ascent from the abstract to the concrete” in relation to the abstract fine arts. An analogy is drawn between the concepts of “abstract” and “concrete” in philosophy and fine arts. The interpretations of these categories by Hegel and К. Marx are indicated. The authors distinguished the features that determine the abstract essence of a work of fine art, including thematic content, expressiveness, emotionality and communication. The systematization of works of art according to the degree of abstraction is proposed. The names of the most typical representatives of various directions of abstract art are given.
Tabular: Probabilistic Inference from the Spreadsheet

Tabular is a domain-specific language for expressing probabilistic models of relational data. Tabular has several features that set it apart from other probabilistic programming languages including: (1) programs and data are stored as spreadsheet tables; (2) programs consist of probabilistic annotations on the relational schema of the data; and (3) inference returns estimations of missing values and latent columns, as well as parameters. Our primary implementation is for Microsoft Excel and relies on Infer.NET for inference. Still, the language can be called independently of Excel and can target alternative inference engines.
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment

Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse." Here we investigate a semi-supervised ensemble learning strategy to produce generalizable blind image quality assessment models. We train a multi-head convolutional network for quality prediction by maximizing the accuracy of the ensemble (as well as the base learners) on labeled data, and the disagreement (i.e., diversity) among them on unlabeled data, both implemented by the fidelity loss. We conduct extensive experiments to demonstrate the advantages of employing unlabeled data for BIQA, especially in model generalization and failure identification.
Understanding to Future of Artificial Psychology System: A Survey

The purpose of this paper is to introduce a very uncommon topic that is rising to strike the symbolization of human thinking on the level of actual thinking.'Artificial Psychology' the integrity of the future, the field clutches a variety of other artificial intelligence systems and also human psychology.Besides being important advancement in the field of AI, Artificial Psychology also outrivals in the human-computer-interaction, health care, e-learning, automotive, security, and many more.In this paper, a core survey of the papers and articles are put out, in order to identify and understand the first footstep onto the implementation of an existing Artificial Psychology System that overcomes the hurdles and gains the critical understandings of the human brain and also processes accordingly.
Let the DOCTOR decide whom to test: Adaptive testing strategies to tackle the COVID-19 pandemic

A robust testing program is necessary for containing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low-resource developing countries), designing an optimal testing program/strategy is a challenging problem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symptomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing program’s effectiveness. In this paper, we address these limitations by making five novel contributions. (i) We formally define the optimal testing problem and propose the DOCTOR POMDP model to tackle it. (ii) We solve the DOCTOR POMDP using a scalable Monte Carlo tree search based algorithm. (iii) We provide a rigorous experimental analysis of DOCTOR’s testing strategies against static baselines - our results show that when applied to the city of Santiago in Panama, DOCTOR’s strategies result in ~40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. (iv) In addition, we analyze DOCTOR’s testing policy to derive insights about the reasons behind the optimality of DOCTOR’s testing policy. (v) Finally, we characterize conditions (of the real world) under which DOCTOR’s optimization would be of most benefit to government policy makers, and thus requires significant attention from researchers in this area. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Face Recognition Using Image Processing for Visually Challenged

Our Eyes encourages us to identify the face and perceive the individuals. For dazzle and outwardly tested individuals, particularly if the individual's eye veins and retina are harmed, it gets unfit to do as such. The innovation has been advancing furiously and with each new development of science and innovation, the simplicity of living is expanding yet barely it tackles the man's physical powerlessness. Building up fixing for the blind and outwardly impeded individuals is definitely not a newly advanced issue. In any case, building up a computer-based answer for such a goal is creating a zone. The computer vision zone is on its way to arrive at the top in creating the vision for robots yet not a replacement for human sight. The target of this framework is to help the client to recognize the individuals without the assistance of a third individual or without the individual being presented himself. In spite of the fact that there are different works utilizing computer vision procedures yet there is no current technique that comprehends all the essential prerequisites for a visually impaired individual. All the current frameworks are grown distinctly for a particular need. In this paper, we set forward another hypothetical plan which joins the significant essential capacity and included some additional capacities for supporting outwardly hindered individuals. This new framework may help some of the crucial issues of blind people that are as yet present. Likewise, we delineate troubles which we are countering during the current research which may require further innovative work. We have built up a face acknowledgment application in raspberry pi with help for sound yield and report the outcomes to the client (daze individual), which tackle that the proposed strategy is promising for constant usage.
Blockchain-Based Untrusted Business Process Monitoring with Optimized Computational Infrastructure

The BPM – Business Process management has been correlated with the designing, monitoring, execution, and enhancement of the concerned business. The inter-organization process possesses a lot of challenges like combined design and mutual trust lacking. Blockchain (BC) which is an emerging methodology plays a vital role in the execution of the trusty process between the inter-organizations. It could able to generate mutual trust between the nodes through innovative specific algorithms with key aspects that motivate the market mechanism. Few of the blockchain system provided a computational structure for the run of the autonomous program known as smart contracts. The study addressed the basic issue of trust in the execution of the collaborative process with the employment of blockchain. The study developed a method for the integration of BC into choreography where no central authority has been required but there exists the maintenance of trust. The study comprised the integration of a complex set of components that permits the coordination or monitoring of the corresponding business process. The study also implemented a reliable solution for the demonstration of the feasibility of the proposed system. The study evaluation comprised the creation of more than 600 smart contracts and the execution of over 10,000 blockchain transactions.
Broadening volcanic eruption forecasting using transfer machine learning

&amp;lt;p&amp;gt;Sudden steam-driven eruptions at tourist volcanoes were the cause of 63 deaths at Mt Ontake (Japan) in 2014, and 22 deaths at Whakaari (New Zealand) in 2019. Warning systems that can anticipate these eruptions could provide crucial hours for evacuation or sheltering but these require reliable forecasting. Recently, machine learning has been used to extract eruption precursors from observational data and train forecasting models. However, a weakness of this data-driven approach is its reliance on long observational records that span multiple eruptions. As many volcano datasets may only record one or no eruptions, there is a need to extend these techniques to data-poor locales.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Transfer machine learning is one approach for generalising lessons learned at data-rich volcanoes and applying them to data-poor ones. Here, we tackle two problems: (1) generalising time series features between seismic stations at Whakaari to address recording gaps, and (2) training a forecasting model for Mt Ruapehu augmented using data from Whakaari. This required that we standardise data records at different stations for direct comparisons, devise an interpolation scheme to fill in missing eruption data, and combine volcano-specific feature matrices prior to model training.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We trained a forecast model for Whakaari using tremor data from three eruptions recorded at one seismic station (WSRZ) and augmented by data from two other eruptions recorded at a second station (WIZ). First, the training data from both stations were standardised to a unit normal distribution in log space. Then, linear interpolation in feature space was used to infer missing eruption features at WSRZ. Under pseudo-prospective testing, the augmented model had similar forecasting skill to one trained using all five eruptions recorded at a single station (WIZ). However, extending this approach to Ruapehu, we saw reduced performance indicating that more work is needed in standardisation and feature selection.&amp;lt;/p&amp;gt;
An Analysis The Factors of Students’ Difficulties in Speaking English Faced by non-English Department Students (A Qualitative Research at Faculty of Education and Teacher Training UIN Sultan Maulana Hasanuddin Banten)

Vini Gusviyani.161230027. 2020. An Analysis The Factors of Students’ Difficulties in Speaking English Faced by non-English Department Students (A Qualitative Research at Faculty of Education and Teacher Training UIN Sultan Maulana Hasanuddin Banten)The purpose of this research is to find out The Factors of Students’ Difficulties in Speaking English Faced by non-English Department Students. This research was conducted with qualitative research, with using survey method. This research used google form as questionnaire to gain the data. The respondents of this research are the 100 students of non-English department at Faculty of Education UIN SMH Banten. The result of this research showed that there were many factors that students have in English speaking difficulties. They are factor anxiety does not master the vocabulary has value 59% agreed, then factor shy of being an intention has value 57% agreed, then factor lack of confident has value 54% agreed, factor lack of self motivation has value 54% agreed, factor of environmental effect has value 54% agreed, factor there is no preparation has value 53% agreed, then factor fear of wrong pronouncing has value 52% agreed, factor of fear of use the wrong words has value 49% agreed, factor affective of students has value 48% agreed, and the last factor anxiety of being bullied has value 44% agreed. It means students have some factors in speaking problem that should be take more attention.
Native Implementation of Mutable Value Semantics

Unrestricted mutation of shared state is a source of many well-known problems. The predominant safe solutions are pure functional programming, which bans mutation outright, and flow sensitive type systems, which depend on sophisticated typing rules. Mutable value semantics is a third approach that bans sharing instead of mutation, thereby supporting part-wise in-place mutation and local reasoning, while maintaining a simple type system. In the purest form of mutable value semantics, references are second-class: they are only created implicitly, at function boundaries, and cannot be stored in variables or object fields. Hence, variables can never share mutable state. Because references are often regarded as an indispensable tool to write efficient programs, it is legitimate to wonder whether such a discipline can compete other approaches. As a basis for answering that question, we demonstrate how a language featuring mutable value semantics can be compiled to efficient native code. This approach relies on stack allocation for static garbage collection and leverages runtime knowledge to sidestep unnecessary copies.
Design And Development of Forest Fire Video Analyzer

Forest fires detection algorithms is an important issue, because some of the objects have the same features with fire, which may result in false alarms. It’s difficult to use fire detection sensor inside the forest. Using fire sensor in forest is very costly. This project will present a forest fire detection method. Firstly, forest fire video analyzer will resize the video. Secondly, it will be converting the particular moving regions from RGB to HSV color space. It will notify the forest department after fire detection through mail. After the fire detection an alarm will be beeping. In this project image processing will be used for modern days digital videography, the digital camera can give clarity and excellent videography, the response time of image processing methods is better than the fire sensor systems. Forest fire video analyzer will be using Object Based Image Analysis algorithm (OBIA) in this project. Python programming language and OPENCV image processing tool will be used for converting images from RGB to HSV. Forest fire video analyzer will also use NUMPY image processing tool for flip the image into a proper direction.
An Analysis of the Recent Visibility of the SigDial Conference

Automated speech and text interfaces are continuing to improve, resulting in increased research in the area of dialogue systems. Moreover, conferences and workshops from various fields are focusing more on language through speech and text mediums as candidates for interaction with applications such as search interfaces and robots. In this paper, we explore how visible the SigDial conference is to outside conferences by analysing papers from top Natural Langauge Processing conferences since 2015 to determine the popularity of certain SigDial-related topics, as well as analysing what SigDial papers are being cited by others outside of SigDial. We find that despite a dramatic increase in dialogue-related research, SigDial visibility has not increased. We conclude by offering some suggestions.
Quantum harmonic oscillators and Feynman-Kac path integrals for linear diffusive particles

We propose a new solvable class of multidimensional quantum harmonic oscillators for a linear diffusive particle and a quadratic energy absorbing well associated with a semi-definite positive matrix force. Under natural and easily checked controllability conditions, the ground state and the zero-point energy are explicitly computed in terms of a positive fixed point of a continuous time algebraic Riccati matrix equation. We also present an explicit solution of normalized and time dependent Feynman-Kac measures in terms of a time varying linear dynamical system coupled with a differential Riccati matrix equation. A refined non asymptotic analysis of the stability of these models is developed based on a recently developed Floquet-type representation of time varying exponential semigroups of Riccati matrices. We provide explicit and non asymptotic estimates of the exponential decays to equilibrium of Feynman-Kac semigroups in terms of Wasserstein distances or Boltzmann-relative entropy. For reversible models we develop a series of functional inequalities including de Bruijn identity, Fisher's information decays, log-Sobolev inequalities, and entropy contraction estimates. In this context, we also provide a complete and explicit description of all the spectrum and the excited states of the Hamiltonian, yielding what seems to be the first result of this type for this class of models. We illustrate these formulae with the traditional harmonic oscillator associated with real time Brownian particles and Mehler's formula. The analysis developed in this article can also be extended to solve time dependent Schrodinger equations equipped with time varying linear diffusions and quadratic potential functions.