Reviewerly/exHarmony huggingface dataset:
Acceleration of Web Interface Generation Through Voice Commands

Abstract Advancement in technology is useless unless it is accompanied by a greater access to technology. Be it lack of technical knowledge or a dearth of ability to comprehend or reason, technology must allow low-code or no-code access to all. This work tries to bridge this accessibility barrier by bringing Natural Language Processing (NLP) to the website development process. using NLP and Azure Cognitive Services, we will be able to understand what elements the user wants to add to the website by extracting the required information. Dynamic generation of web pages using voice commands from the user will not only increase low-code accessibility to naive users, but will also speed up work for experienced users by allowing them to easily add elements to the website.
Quantization Algorithms for Random Fourier Features

The method of random projection (RP) is the standard technique in machine learning and many other areas, for dimensionality reduction, approximate near neighbor search, compressed sensing, etc. Basically, RP provides a simple and effective scheme for approximating pairwise inner products and Euclidean distances in massive data. Closely related to RP, the method of random Fourier features (RFF) has also become popular, for approximating the Gaussian kernel. RFF applies a specific nonlinear transformation on the projected data from random projections. In practice, using the (nonlinear) Gaussian kernel often leads to better performance than the linear kernel (inner product), partly due to the tuning parameter $(\gamma)$ introduced in the Gaussian kernel. Recently, there has been a surge of interest in studying properties of RFF. After random projections, quantization is an important step for efficient data storage, computation, and transmission. Quantization for RP has also been extensive studied in the literature. In this paper, we focus on developing quantization algorithms for RFF. The task is in a sense challenging due to the tuning parameter $\gamma$ in the Gaussian kernel. For example, the quantizer and the quantized data might be tied to each specific tuning parameter $\gamma$. Our contribution begins with an interesting discovery, that the marginal distribution of RFF is actually free of the Gaussian kernel parameter $\gamma$. This small finding significantly simplifies the design of the Lloyd-Max (LM) quantization scheme for RFF in that there would be only one LM quantizer for RFF (regardless of $\gamma$). We also develop a variant named LM$^2$-RFF quantizer, which in certain cases is more accurate. Experiments confirm that the proposed quantization schemes perform well.
Label-Guided Learning for Item Categorization in e-Commerce

Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users.One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task.We have conducted a systematic investigation of the potential benefits of these methods on a real data set from Rakuten, a major e-commerce company in Japan.We found that using pre-trained word embeddings specialized to specific categories of items performed better than one obtained from all available categories despite the reduction in data set size.Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding.These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.
Generating and Optimizing Human-Readable Quantitative Program Trading Strategies through a Genetic Programming Framework

In this paper, we provide a highly flexible genetic programming framework for automatic generation and optimization of program trading strategies. We propose the input/output modules and their implementation methods, decoupled from the GP kernel, making it a priori-posteriori framework for trading practitioners. For human-readable purposes, we also give various empirical regularization methods, including NSGA-II multi-objective selection, as well as experimentally effective performance measures.
A Study On Recruitment Process At Msys Technologies

The purpose of this study was to find the effectiveness of the employee satisfaction level towards the recruitment processes of organization and also to assess the impact of recruitment process on organizational performance. The findings of the study helps to improvise the strategies to make the recruitment process effective. The sample for this study is collected nearly from 100 employees at MSys Technologies were personally surveyed with a structured questionnaire. Based on the investigation in recruitment, research questions were answered. In this study Descriptive Research Design and inferential analysis was used as the sampling procedure and data obtained were statistically analyzed using Chi-square test, one way Anova, Co-efficient of correlation. The analysis and interpretation helps to identify the findings and suggestions of the workers. The more objective the recruitment criteria, the better the organization’s performance.
A Survey of Blockchain and E-governance applications: Security and Privacy issues

In a world full of e-services that provide speed of implementation and comfort to customers and users and provide high-luxury features, whether in the world of stock's trading or e-government systems or others. However, any system can suffer from serious challenges such as security and privacy issues, so the existing infrastructure of e-Governance paves the way for a powerful underlying technology. Using Blockchain technology is a strong option to secure platforms and services. Therefore, a new framework that integrates Blockchain into any platform is proposed, especially in Saudi Arabia. This paper surveys research related to Blockchain technology and developments of its witch cut across both public and private sectors. The adoption and integration of e-governance is linked as a key point towards smart applications in the smart environment is discussed. For the improvement and development of any country, there is a need to connect technology-based services and products. These are the main points of an intelligent environment with e-governance in any area. This work refers to these views of e-governance and online services.
Locally Private k-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error

Given a data set of size n in d'-dimensional Euclidean space, the k-means problem asks for a set of k points (called centers) such that the sum of the l_2^2-distances between the data points and the set of centers is minimized. Previous work on this problem in the local differential privacy setting shows how to achieve multiplicative approximation factors arbitrarily close to optimal, but suffers high additive error. The additive error has also been seen to be an issue in implementations of differentially private k-means clustering algorithms in both the central and local settings. In this work, we introduce a new locally private k-means clustering algorithm that achieves near-optimal additive error whilst retaining constant multiplicative approximation factors and round complexity. Concretely, given any c&gt;√2, our algorithm achieves O(k^(1 + O(1/(2c^2-1))) √(d' n) log d' poly log n) additive error with an O(c^2) multiplicative approximation factor.
Mobile Application Development Methodologies Adopted in Omani Market: A Comparative Study

Popularity of mobile phones and huge growing for mobile applications make developers in need for flexible software process, which can deal with many challenges facing the mobile app development process. These challenges include: volatility of requirements, strong user involvement, development time tightness, process simplicity, and production of valuable software in low cost. This research study investigates the current mobile app development approaches adopted in Omani market and provides a comparison between existing methods. The results reveal that Agile approach is the most popular model for mobile software engineering in Omani, as it naturally fits most of the applications required in this market. The study also discusses various agile process models such as Scrum, XP, Lean, DSDM, and others. It is concluded that XP model is the most preferable model used by Omani developers due to its dynamic and adaptive nature for different mobile app processes. The study provides also a series of recommendations for mobile app developers which should help in selecting the most appropriate method that suits the targeted market sector.
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness

Stochastic gradient algorithms are often unstable when applied to functions that do not have Lipschitz-continuous and/or bounded gradients. Gradient clipping is a simple and effective technique to stabilize the training process for problems that are prone to the exploding gradient problem. Despite its widespread popularity, the convergence properties of the gradient clipping heuristic are poorly understood, especially for stochastic problems. This paper establishes both qualitative and quantitative convergence results of the clipped stochastic (sub)gradient method (SGD) for non-smooth convex functions with rapidly growing subgradients. Our analyses show that clipping enhances the stability of SGD and that the clipped SGD algorithm enjoys finite convergence rates in many cases. We also study the convergence of a clipped method with momentum, which includes clipped SGD as a special case, for weakly convex problems under standard assumptions. With a novel Lyapunov analysis, we show that the proposed method achieves the best-known rate for the considered class of problems, demonstrating the effectiveness of clipped methods also in this regime. Numerical results confirm our theoretical developments.
Interpretable Stability Bounds for Spectral Graph Filters

Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-structured data, spectral graph filters have been used to solve common tasks such as denoising and anomaly detection, as well as design deep learning architectures such as graph neural networks. Despite being an important tool, there is a lack of theoretical understanding of the stability properties of spectral graph filters, which are important for designing robust machine learning models. In this paper, we study filter stability and provide a novel and interpretable upper bound on the change of filter output, where the bound is expressed in terms of the endpoint degrees of the deleted and newly added edges, as well as the spatial proximity of those edges. This upper bound allows us to reason, in terms of structural properties of the graph, when a spectral graph filter will be stable. We further perform extensive experiments to verify intuition that can be gained from the bound.
Selection and evaluation of polar cruise fin stabilizer based on combination weighting-TOPSIS method

Objectives Many factors should be taken into consideration in selection of fin stabilizer for polar cruise, such as environmental adaptation and device performance, the optimal fin stabilizer was difficult to choose by subjective judgment. Aiming at the selection of fin stabilizers for polar cruise, a comprehensive selection and evaluation method based on combination weighting-TOPSIS method was proposed. Methods The selection principle of fin stabilizer for 8 035 gross ton polar cruise was confirmed by PC6 rules for polar cruise, and the initial area of fin stabilizer was calculated by experimental mathematical model. Three design schemes were put forward for polar cruise fin stabilizer. Selection and evaluation index system was built by consulting experts, which include 5 primary indexes and 14 secondary indexes. The weights of the indexes were determined by analytic hierarchy process (AHP) and entropy weight method(EWM); technique for order preference by similarity to ideal solution(TOPSIS) was introduced to calculate the degree of closeness between the alternative and the ideal solution. Results According to the ranking results, the design scheme 1 was selected as the optimal fin stabilizer meeting the decision maker's requirement. Conclusions This method has some guiding significance for the selection of fin stabilizer for polar cruise and the builder and decision make's blindness was reduced.
SEECing a Solution: A recipe for the selection and evaluation of electronic content

This conference presentation (PowerPoint slides) supports the talk 'SEECing a solution: using our selecting and evaluating electronic content tool for evidence based decision making'.
News Article Based Industry Risk Index Prediction for Industry-Specific Evaluation

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.
Model Pengembangan Sistem Informasi Pendaftaran Online Pasien Pada Klinik Gigi Dentika Berbasis User Centred Design

In today's online-based era, the use of websites in carrying out patient registration procedures is very useful. Dental Clinic Dentika is one of the dental clinics located in the Tangerang area and has quite a number of patients. In the procedure for registering patients for examination, they still use a manual system, where patients come directly and wait in line for the examination. This is deemed ineffective because long lines can occur at a certain time. This web-based online patient registration is to make it easier for patients to register themselves when they are going to take medication or dental care without having to come to the clinic first. Another advantage is that it makes it easier for clinical administrative staff to serve patient registration, increases time efficiency in terms of patient care and facilitates patient data management. The User Centered Design (UCD) method is one of the right methods to solve the problem of patient registration at the Dentika Dental Clinic. This method looks at the needs of the user or users in designing an information system. The result of this study is a website that can be accessed by patients to register for the examination
Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage

Signal denoising---also known as non-parametric regression---is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed domain corresponds to smoothing in the original domain. A key question in such applications is how much to shrink, or, equivalently, how much to smooth. Empirical Bayes shrinkage methods provide an attractive solution to this problem; they use the data to estimate a distribution of underlying hence automatically select an appropriate amount of shrinkage. However, most existing implementations of Empirical Bayes shrinkage are less flexible than they could be--both in their assumptions on the underlying distribution of effects, and in their ability to handle heterskedasticity---which limits their signal denoising applications. Here we address this by taking a particularly flexible, stable and computationally convenient Empirical Bayes shrinkage method, and we apply it to several signal denoising problems. These applications include smoothing of Poisson data and heteroskedastic Gaussian data. We show through empirical comparisons that the results are competitive with other methods, including both simple thresholding rules and purpose-built Empirical Bayes procedures. Our methods are implemented in the R package smashr, SMoothing by Adaptive SHrinkage in R, available at this https URL
Numerical Formula Recognition from Tables

Claims over the numerical relationships among some measures are commonly expressed in tabular forms, and widely exist in the published documents on the Web. This paper introduces the problem of numerical formula recognition from tables, namely recognizing all numerical formulas inside a given table. It can well support many interesting downstream applications, such as numerical error correction in tables, formula recommendation in tables. Here, we emphasize that table is a kind of language that adopts a different linguistic paradigm from natural language. It uses visual grammar like visual layout and visual settings (e.g., indentation, font style) to express the grammatical relationships among the table cells. Understanding tables and recognizing formulas require decoding the visual grammar while simultaneously understanding the textual information. Another challenge is that formulas are complicated in terms of diverse math functions and variable-length of arguments. To address these challenges, we convert this task into a uniform framework, extracting relations of table cell pairs in a table. A two-channel neural network model TaFor is proposed to embed both the textual and visual features for a table cell. Our framework achieves the formula-level F1-score = 0.90 on a real-world dataset of 190179 tables while a retrieval-based method achieves F1-score = 0.72. We also perform extensive experiments to demonstrate the effectiveness of each component in our model, and conduct a case study to discuss the limits of the proposed model. With our published data this study also aims to attract the community's interest in deep semantic understanding over tables.
Open Data Policies Analysis Disputes Mediation Cases in Korea: Based on OUR Data Index and ODB

Data is a critical driving force for industrial development and new value creation in the fourth industrial revolution era. As a result, the government's disclosure of data to the public has become an issue worldwide, leading to an increase in the trend of open data. Simultaneously, in providing and using data due to open data policies, disputes increasingly arise due to the refusal or discontinuation of data provided by public institutions. To solve this problem, policies were introduced to mediate disputes over the provision of public data. This paper analyzes the policies on Mediation of Disputes over the Provision of Public Data, which is part of The Republic of Korea's open data policy, and its cases, and examines the current policies on Mediation of Disputes over the Provision of Public Data in The Republic of Korea. It also uses the OECD's Open, Useful, Reusable (OUR) Data Index and WWW Foundation's Open Data Barometer (ODB) to identify The Republic of Korea's policies on Mediation of Disputes over Provision of Public Data and to identify its excellence and improvements. It can be seen as the first paper to use two globally accepted indexes to analyze cases of public data dispute mediation, identify current circumstances, and present their excellence and direction of improvement. At the same time, we explain to the open government latecomers what they should benchmark through The Republic of Korean cases.
The role of media while teaching youngster learners

This article surveys a choice of considers on advanced media and learning for little youngster ages 3 to 6. The scope of advanced media for this age group is developing and incorporates PC conveyed and online exercises; support video games; handheld media, occasionally with GPS or an accelerometer, in cell phones and other remote mobile devices; electronic toys and learning systems; dolls and robots that move and talk; physically active games using dance pads, sports equipment, or stationary bikes as the interface to the game; and online communities and social networks; among others. The examination has tried the adequacy of a few advanced media items for little youngsters’ learning and has found, for instance, improvements in kids’ information and abilities for speculation, arranging, noticing, critical thinking, perusing, language, math, hypothesis arrangement and testing, innovativeness, and collaborative learning. In any case, large numbers of the most popular commercially accessible advanced media items for small kids have never been studied or tested, thus there is no authoritative proof of their advantages or drawbacks. The article concludes with a conversation of examination moves toward that could be utilized to research small kids’ preparing of advanced media to improve the plan and adequacy of future media items planned for this age group.
Analisis Pola Curah Hujan Terhadap Produktifitas Tanaman Padi Sawah di Provinsi Jawa Barat

Changes in rainfall that are quite volatile cause agricultural productivity, especially for wetlandpaddy, is difficult to predict. The importance of predictions is made in order to prevent the decline inproductivity of paddy that have an impact on the welfare of the people in Indonesia. This study aims tolook at changes in rainfall patterns associated with the productivity of wetland paddy. The data usedin this study is in the form of rainfall and wetland paddy production in 2002-2015. The method u`sedin this research is descriptive analysis related to pattern analysis and determination of data patternsusing spectral analysis which can be seen from the results of peroidigrams. The results of this studyare the pattern of rainfall that is repeated based on the periodogram analysis. Rainfall patterns inWest Java Province occur every 33 months or 3 years. Increased productivity of wetland paddyoccurred in 2006, 2009,2012, 2015 in accordance with the pattern of rainfall that occurs every 3years. The characteristics of rainfall types in West Java Province in 2002-2015 are in the range ofslightly wet to very wet.
Developing and perfecting universal cognitive skills of students through integrating scientific knowledge about information in the learning content

The learning algorithms for working with textual information, proposed by the authors of the article, allow not only to speed up the process of understanding and consolidating the course content but also provide for a self-reflection component of students' activity, which makes it possible to control, correct and modify the algorithm under the influence of changing communication conditions.