Reviewerly/exHarmony huggingface dataset:
JSONPS: Secure an inherently insecure practice with this one weird trick!

JSONP, or JSON with Padding, is a cross-origin data exchange mechanism that was proposed in 2005 as a reaction to the extremely strict security model employed by browsers of the day [10]. At that time, the Same-Origin Policy (SOP) prohibited any kind of cross-site communication for security purposes. However, at the same time, this prevented legitimate cross-origin scenarios in which two websites mutually exchange data with each other. JSONP is a workaround that circumvents the SOP by leveraging cross-origin script inclusions to pass data from a backend to a cross-origin webpage. In modern browsers, Cross-Origin Resource Sharing and the PostMessage API offer more powerful and more secure ways to exchange data. However, due to its popularity, its framework support, and a large amount of legacy applications and APIs, JSONP is still very popular today. According to our study, at least 10 % of the Alexa top sites still leverage JSONP. This is problematic since JSONP is inherently insecure: To load data from a cross-origin backend, a webpage has to include and execute a script within its security context. Due to the SOP, the website cannot verify the contents of the script and thus has to blindly grant the third-party server code execution capabilities. To counter this problem, this paper presents JSONPS – a light-weight sandbox for JSONP scripts. By leveraging browser features such as iframes and the PostMessage API, the sandbox enables a website to execute a JSONP script outside of its security boundaries, while still being able to retrieve the data in a secure way. The sandbox can be deployed to large legacy code bases and modern application frameworks with minimal code changes. Furthermore, it considerably increases security when the usage of JSONP is inevitable for backward-compatibility reasons. The sandbox is designed to work in older browsers: At Google, we have deployed the sandbox to major applications serving traffic to millions of users.
The Increasing of Students’ English Speaking by Using Communicative Language Teaching (CLT) through Online Learning in Pandemic Covid-19

This study dealt students’ English speaking by using Communicative Language Teaching (CLT) through Online Learning in Pandemic Covid-19. It is used qualitative research design. It was conducted descriptively. The purpose of this study was to know how the increasing of students’ English speaking by using Communicative Language Teaching (CLT) through Online Learning in Pandemic Covid-19. There were 30 students of AMIK Medicom Medan as sample in this study. There were two instrument that conducted in this study, they were observation and interview. The data were analyzed by Miles, Huberman and Saldana (2014) analysis. The result of this study was Communicative Language Teaching (CLT) had good role on teaching learning process. The students’ speaking increased significantly. The students talked more in this learning method. Then, the students felt happy and comfortable in teaching learning process. It can be proved by result of interview. CLT was able to make the students self confidence in practicing their speaking in front of crowded people and be good public speaking.
Secure File Storage in Cloud Computing Using a Modified Cryptography Algorithm

This chapter investigates the security issues identified with the file cloud storage to ensure the security of client information in cloud information server. The authors have proposed a modified RSA algorithm with multiple keys and CRT to ensure confidentiality of data coupled with hashing through SHA-512 to maintain integrity. This work has made a secure data exchange app where files are encrypted using the RSA-CRT algorithm and hashed later. On successfully implementing the work, they observed that the proposed technique is more secure than the original RSA algorithm and RSA-CRT. Furthermore, it enhanced the algorithm performance for decryption because it employed the CRT for decryption; thus, the proposed technique proved to be faster than RSA with multi keys.
Feedback Error Learning Control Using OS-ELM for SI Engine Airpath Systems

Currently, due to the stricter emission legislation, spark-ignition (SI) engine with low pressure exhaust gas recirculation (LP-EGR) has become a research hotspot. However, with the increasing complexity of engine, it is difficult to obtain sufficient control performance by conventional MAP control or PID control. This paper focuses on the airpath system of a SI engine with low pressure EGR, proposes a new model-based control method for the model of the airpath system based on the Feedback Error Learning control using online sequential-extreme learning machine (OS-ELM) as controller algorithm. In this paper, the airpath model has been built and effectiveness of the proposed control method has been confirmed on the model.
Toward Using Machine Learning-Based Motion Gesture for 3D Text Input

In this paper, we discuss our implementation of a gesture-based 3-dimensional typing system within virtual reality. Rather than the conventional point-and-click keyboard commonly found in immersive technologies, we explore using unique gestures with the controller to enter a specific key. To map these gestures and movements to their respective key, we utilize machine learning techniques to avoid naive hard-coded implementations. The result of the trained model is a text input system that adapts to the user’s gestures, rather than forcing the user to conform to the system’s definition of a gesture. Our goal is to work toward a viable alternative to standard virtual reality keyboards and typing systems.
Multi-view Urban Scene Reconstruction with Continuous Adjustment

Accurate 3D urban scene reconstruction from a set of multi-view images is an important and challenging task in various fields such as computer visions and computer graphics. The previous methods are usually in a dilemma when considering the problems of completeness and accuracy, especially for the complex outdoor scenes. To address these issues, in this paper we propose a new framework based on a number of available methods. Apart from joint optimization with image semantic segmentation, we also attempt to introduce a unified evaluation approach for 3D reconstruction. We will fill holes and remove noise with the guidance of semantic information and the evaluation. The aim of our framework is to improve the quality of urban scene reconstruction in terms of completeness and accuracy for point clouds, which are obtained with state-of-art methods. We demonstrate the effectiveness of our framework on several real public available datasets. As shown in the experimental results, our proposed framework can obtain a better reconstructed model than state-of-art methods.
Cybersecurity in Accounting Research

Purpose – This paper aims to update the cybersecurity-related accounting literature by synthesizing 39 recent theoretical and empirical studies on the topic. Furthermore, the paper provides a set of categories into which the studies fit.Design/methodology/approach – This is a synthesis paper that summarizes the research literature on cybersecurity, introducing knowledge from the extant research and revealing areas requiring further examination.Findings – This synthesis identifies a research framework that consists of the following research themes: cybersecurity and information sharing; cybersecurity investments; internal auditing and controls related to cybersecurity; disclosure of cybersecurity activities; and security threats and security breaches.Practical implications – Academics, practitioners, and the public would benefit from a research framework that categorizes the research topics related to cybersecurity in the accounting field. This type of analysis is vital to enhance the understanding of the academic research on cybersecurity and can be used to support the identification of new lines for future research.Originality/value – This is the first literature analysis of cybersecurity in the accounting field, and it has significant implications for research and practice by detailing, for example, the benefits of and obstacles to information sharing. This synthesis also highlights the importance of the model for cybersecurity investments. Further, the review emphasizes the role of internal auditing and controls to improve cybersecurity.
Combining Task and Motion Planning through Rapidly-Exploring Random Trees

Combined task and motion planning is a relevant issue in robotics. In path and motion planning, Rapidly-exploring Random Trees (RRTs) have been proposed as effective methods to efficiently search high-dimensional spaces. On the other hand, the deployment of these techniques to symbolic task planning problems has been partially investigated. In this paper, we explore this issue proposing a method to combine task and motion planning based on RRTs. Our approach relies on a metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and obstacle-free trajectories. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in pick-carry-and-place tasks.
Using Marketing Technologies to Increase User Engagement on Library’s Social Media Pages

A significant portion of the online activities of a modern man is associated with the space of social media, where his need to demonstrate his competence, to be recognized by other users, and to possess information is realized. For libraries, such a space is a vital tool of ensuring interactions with users, allowing them the organization of activities to inform subscribers, create and publish content related both to the promotion of library and information resources and services, and to the formation of the image of the library as a whole. However, there are problems related to user engagement on pages and in library groups. The purpose of this work is to describe marketing technologies that allow the creation of favorable conditions for the development of discussion among users of social networks and increasing their activity on the pages of libraries, which has a favorable effect on the coverage of basic, informational records. Contests, quizzes and promotions are offered as such technologies. An analysis of the ‘VKontakte’ pages of the fourteen largest libraries in Russia showed that these forms are not widespread enough in the online environment: libraries prefer to advertise offline promotions, contests and quizzes, rather than conduct them on the social media platform. The following important criteria for the implementation of these technologies are identified: the use of direct appeals to the audience with the positive emotional coloring; topics that allow one to show his social activity, as well as those close to the interests of the audience. Thus, these criteria should be taken into account when libraries conduct contests, quizzes and promotions in social media.
Beauty Moment Rendering via Face Happiness Scoring

Nowadays, with the rapid development of technology and an energetic lifestyle, people tend to use electronic devices to capture moments. Thus, the need to have a delightful selfie is increasing. This work introduces a Beauty Moment Rendering via Face Happiness Scoring framework, which aims to generate a key-frame that summarizes a short selfie video. Given a set of consecutive photos as input, our approach selects a single image and renders a face for each person at their happiest moment. In order to select a suitable key-frame, we decide to combine the eyes and emotion information as our aggregated score. First, the method detects and tracks the faces in the video frames. Then, the face that has the highest score of each person will be rendered on the result key-frame with the eye gazes being redirected based on a generative model. To the best of our knowledge, we are among the first to address the problem, which comprises video summarization and face beautification.
The Design Perspective

This chapter of the book contains a first exploration of those defined as ‘collaborative design practices’ within private organisations obtained through a set of interviews and observational studies carried out with different kinds of stakeholders within private organisations. The first round of interviews outlined in the first Sect. 2.1 a set of different interpretations and applications of design thinking in the world of practice. Those have been clustered in what have been called ‘areas of blur’. They refer to: (i) the goal of the activities, (ii) their subject-matter and (iii) their impact. Section 2.2 showcases a series of observations of collaborative design practices. Those observations composed and informed a comparative analysis that was carried out through the identification of a set of variables. The variables refer to specific characteristics of the practices such as: (i) goal of the activities, (ii) variety of participants, (iii) style of guidance and (iv) process design. The observations highlighted correlations between those variables that have been organised in a compass. The compass represents a navigation tool that could support who organises collaborative design practices.
Polysemy, synonymy, and metaphor: The use of the<i>Historical Thesaurus of English</i>in translation studies

Abstract This article proposes that the consideration of translation equivalences for lexical items in English benefits from reference to the diachronic digital resources of Historical Thesaurus of English and its offshoot projects, Mapping Metaphor with the Historical Thesaurus, and the Semantic Annotation and Mark-Up for Enhancing Lexical Searches semantic tagger. These resources are the result of a long-term endeavour to code every sense of every headword in the lexicographical record of the English language. There has hitherto been little research into the relevance of these resources to the translation challenges raised by synonymy, polysemy, and the semantic radiation of lexical items. The relevance of these resources and their potential application to the study of equivalence in translation is illustrated with reference to a selection of semantic domains in a Portuguese and a Chinese translation of Jack London’s The Call of the Wild.
Research on Key Technologies of integration of agricultural and rural information service cloud platform

Information technology has become the most critical technical factor to promote economic development and social progress. With the application of biotechnology and information technology in agriculture to deepen modern agriculture, information agriculture will become more important. It is the basic requirement of building a well-off society in an all-round way and the basic content of agricultural modernization to transform traditional agriculture with modern information technology and combine information technology with traditional agriculture. This paper designs a modern agricultural information service platform, which combines computer network middleware with component technology. This project uses B/S structure, Java as programming language, Struts as development framework, Oraci 9i as background database. In recent years, many achievements have been made in cloud based information platform construction and cloud service models. It has the feasibility of building rural information service platform to implement agricultural cloud computing; cloud computing technology is developed from distributed processing, grid computing and parallel processing technology. The strong support of the national policy environment, especially the weak social and economic conditions in rural areas, cloud service model of high efficiency, low cost, flexibility and other advantages, has become a powerful driving force for agriculture and rural construction. Information service cloud platform.
Network Embedding with Topology-Aware Textual Representations

Textual network embedding aims to learn meaningful low-dimensional representations for vertices with the consideration of the associated texts. When learning the representations for texts in network embedding, existing methods mostly only exploit information from neighboring texts (i.e., contexts), while rarely taking advantages of the valuable network topological (structural) information. To bridge the gap, in this paper, a model based on adaptive-filter convolutional neural networks (CNN) is developed, in which the filters are adapted to local network topologies, rather than clamped to fixed values as in traditional CNNs. The dependency enables the learned text representations to be aware of local network topologies. It is shown that the proposed topology-aware representations can be viewed as a complement to existing context-aware ones. When the two are used together, experimental results on three real-world benchmarks demonstrate that significant performance improvements on the tasks of link prediction and vertex classification.
Designing production-friendly machine learning

Building production ML applications is difficult because of their resource cost and complex failure modes. I will discuss these challenges from two perspectives: the Stanford DAWN Lab and experience with large-scale commercial ML users at Databricks. I will then present two emerging ideas to help address these challenges. The first is "ML platforms", an emerging class of software systems that standardize the interfaces used in ML applications to make them easier to build and maintain. I will give a few examples, including the open-source MLflow system from Databricks [3]. The second idea is models that are more "production-friendly" by design. As a concrete example, I will discuss retrieval-based NLP models such as Stanford's ColBERT [1, 2] that query documents from an updateable corpus to perform tasks such as question-answering, which gives multiple practical advantages, including low computational cost, high interpretability, and very fast updates to the model's "knowledge". These models are an exciting alternative to large language models such as GPT-3.
Colorization of Anime Gray Images via Generative Adversarial Networks

In the process of human cognition to the world, coloring plays a decisive role, so gray image coloring is a key topic in the field of digital image processing, has great research significance. More and more deep learning-based coloring methods have been proposed with the development of deep learning and the rapid update and iteration of current large-scale computing devices. The coloring effect is getting better and better. In this context, this paper adopts the gray image shading algorithm based on Generative Adversarial Network (GAN), uses U-Net as the compiler, LeNet as the decoder, and adds non-local blocks after using the convolutional layer of activation function in the first layer of the encoder to extract global features in a better way. At the same time, the color test is carried out on some animation grayscale images. The experimental results show that the method presented in this paper has a good dyeing effect.
Discussion on Decision Tree Algorithm in University Teaching Management System

The Ministry of Education issued (February 4, 2020) the guiding opinions on organizing and managing the online teaching in ordinary colleges and universities during the period of epidemic prevention and control. To jointly implement and protect online teaching in the period of epidemic prevention and control, to achieve the goal of “ceasing teaching and stopping classes without stopping learning”. How to effectively link up online and offline teaching, how to continuously consolidate the effectiveness of online teaching in the post epidemic era is a challenge for university teaching management.
Automatic Watermeter Reading in Presence of Highly Deformed Digits

The task we face in this paper is to automate the reading of watermeters as can be found in large apartment houses. Typically water passes through such watermeters, so that one faces a wide range of challenges caused by water as the medium where the digits are positioned. One of the main obstacles is given by the frequently produced bubbles inside the watermeter that deform the digits. To overcome this problem, we propose the construction of a novel data set that resembles the watermeter digits with a focus on their deformations by bubbles. We report on promising experimental recognition results, based on a deep and recurrent network architecture performed on our data set.
Combining Morphological and Histogram based Text Line Segmentation in the OCR Context

Text line segmentation is one of the pre-stages of modern optical character recognition systems. The algorithmic approach proposed by this paper has been designed for this exact purpose. Its main characteristic is the combination of two different techniques, morphological image operations and horizontal histogram projections. The method was developed to be applied on a historic data collection that commonly features quality issues, such as degraded paper, blurred text, or presence of noise. For that reason, the segmenter in question could be of particular interest for cultural institutions, that want access to robust line bounding boxes for a given historic document. Because of the promising segmentation results that are joined by low computational cost, the algorithm was incorporated into the OCR pipeline of the National Library of Luxembourg, in the context of the initiative of reprocessing their historic newspaper collection. The general contribution of this paper is to outline the approach and to evaluate the gains in terms of accuracy and speed, comparing it to the segmentation algorithm bundled with the used open source OCR software.
SimpleChartsRI: A User-Friendly Web-Tool for Creating Effective Visualizations

Data visualization tools can help teachers engage students who are visual learners. Current web-tools that focus on the creation of visualizations are not beginner-friendly and require little support for teachers wishing to implement visualizations into their classrooms. As a result, teachers rarely use visualization tools. This paper addresses the lack of visualization tool usage in classrooms, analyzes why this is based on previous research and current research we conducted, and offers potential focuses a visualization tool should have—through the lens of our own tool SimpleChartsRI—for pedagogical use. SimpleChartsRI aims to help high-school teachers create effective visualizations through a simple user-friendly interface that guides them on best practices.
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task

Fine-Grained classification models can expressly focus on the relevant details useful to distinguish highly similar classes typically when the intra-class variance is high and the inter-class variance is low given a dataset. Most of these models use part annotations as bounding box, location part, text attributes to enhance the performance of classification and other models use sophisticated techniques to extract an attention map automatically. We assume that part-based approaches as the automatic cropping method suffers from a missing representation of local features, which are fundamental to distinguish similar objects. While Fine-Grained classification endeavours to recognize the leaf of a graph, humans recognize an object trying also to make a semantic association. In this paper, we use the semantic association structured as a hierarchy (taxonomy) as supervised signals and used them in an end-to-end deep neural network model termed as EnGraf-Net. Extensive experiments on three well-known datasets: Cifar-100, CUB-200-2011 and FGVC-Aircraft prove the superiority of EnGraf-Net over many Fine-Grained models and it is competitive with the most recent best models without using any cropping technique or manual annotations.