Reviewerly/exHarmony huggingface dataset:
Programmer Experience: A Systematic Mapping

User eXperience (UX) identifies the perceptions of people over of the use (or even the anticipated use) of a product, system or service. The programmers are users of specific systems, and several types of software development artifacts, such as programming environments and design documents. We performed a systematic mapping about Programmer eXperience (PX). In this work we consider the programmers as a particular type of users of particular artifacts. We therefore consider PX as a particular type of UX. The literature usually focuses on PX from a Software Engineering point of view. We analyzed PX mainly from a Human-Computer Interaction (HCI) perspective. We reviewed articles about PX, empirical studies related to aspects of PX, and studies addressing PX on programming environments. The results show that there is an interest on the PX, but the concept is not yet clearly defined. We found 40 articles published in the last ten years and established that the studies address usability and PX aspects focusing on four topics: (i) programming languages, (ii) programmers' interaction with the integrated development environment, (iii) application programming interfaces, and (iv) articles about programmers' behavior. It is a relatively small numbers of articles, compared with other Software Engineering or HCI areas. This represents a research opportunity for this systematic review and others that can be performed.
Research on Cyberspace Security Education for Teenagers Based on Data Analysis

While the Internet brings great convenience to teenagers' life, study and entertainment, its unsafe environment and bad information content also bring a lot of negative impacts and harms to the healthy growth of teenagers. This paper takes the data as the basis of the research, and finds out the problems existing in the youth network security education through the analysis. This paper discusses the strategies of network safety education for teenagers from the four levels of school, parents, government and enterprises (social organizations), so as to make them form their own "immune" system, thus effectively resisting the threats and confrontations of complex network space and games.
A Computer Algebra System for R: Macaulay2 and the m2r Package

Algebraic methods have a long history in statistics. Apart from the ubiquitous applications of linear algebra, the most visible manifestations of modern algebra in statistics are found in the young field of algebraic statistics, which brings tools from commutative algebra and algebraic geometry to bear on statistical problems. Now over two decades old, algebraic statistics has applications in a wide range of theoretical and applied statistical domains. Nevertheless, algebraic statistical methods are still not mainstream, mostly due to a lack of easy off-the-shelf implementations. In this article we debut m2r, an R package that connects R to Macaulay2 through a persistent back-end socket connection running locally or on a cloud server. Topics range from basic use of m2r to applications and design philosophy.
Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant

Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don't have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user's questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.
Security, Trust and Privacy in Cyber (STPCyber): Future trends and challenges

Today’s world experiences massively interconnected devices to share information across variety of platforms between traditional computers (machines), Smart IoT devices used across smart homes, smart interconnected vehicles etc. and of course the social networks apps such as Facebook, Linkdn, twitter etc. We experience the growth has been skyrocketing and the trend will continue exponentially to the future. At one end, we find life becomes easier with such developments and at the other end; we experience more and more cyber threats on our privacy, security and trustworthiness with organizations holding our data. In this special issue, we summarize contributions by authors in advanced topics related to security, trust and privacy based on a range of applications and present a selection of the most recent research efforts in these areas.
A new disparity map quality assessment based on structural similarity for remotely sensed image pairs

Disparity map quality assessment is crucial to evaluate the accuracies of stereo matching algorithms. Several widely used measures such as root mean square (RMS), mean absolute error (MAE) and bad matching pixels (BMP) have been proposed to evaluate the similarity between disparity map and ground truth (GT) map. These measures are based on grey-scale errors while ignoring the structural similarity between disparity map and GT. In this paper, we propose a united similarity measure based on sructure similarity and normalized RMS (USNR) for disparity map quality assessment. Our new measure is able to simultaneously evaluate grey-scale similarity and region-aware structural similarity between disparity map and GT map. Experiments have been carried out using stereo pair datasets from various scenes, including street view, planetary, natural landscape and urban, to demonstrate the feasibility, reliability and robustness of our measure compared to state-of-the-art evaluation methods.
Designing Information System for Student Practicum Assessment in the Laboratory

Abstract Practicum assessment process at the Laboratory in some university still uses conventional methods ranging from processing practicum value data to presenting assessment information. This certainly can cause various problems that can hamper activities and services in the laboratory. One of the efforts that can be done to improve the practicum assessment process is by designing a practicum assessment information system. The purpose of designing a practicum assessment information system is to identify the information system of current practicum assessments, analyze information system needs, design and build a practicum assessment information system in the Information Systems and Decision Laboratory. The method used in the design of information systems is the Framework for the Application of Systems Thinking (FAST) with the Model Driven Development Strategy model. The FAST method consists of several stages ranging from scope definition, problem analysis, requirements analysis, logical design, decision analysis, physical design, development and testing, implementation and delivery, system operation and maintenance. The design of the proposed improvement of the practicum assessment system in the process block using Data Flow Diagrams (DFD) to get the architecture processes in the practicum assessment system application. The data block uses the Entity Relationship Diagram to design a practicum assessment database system. The communication block uses the Use-Case Diagram design model to get the interface design from the practicum assessment system. The construction of a practicum assessment information system is carried out using XAMPP tools to access the Apache server and the MySQL database and Sublime Text is used to build application interfaces using HTML and PHP programming languages. So that the results of designing the information system for practicum assessment using information technology can be obtained which can be a medium for processing and presenting practical values in the laboratory, which are easily accessible to users and improve aspects of the PIECES framework including performance, information, economy, control, efficiency and service.
Access control approaches for smart cities

As the world population grows along with increase in urbanization, cities are getting more and more populated and increasing demands on various natural and man-made resources. Making cities smart through the appropriate application of the plethora of innovative new technologies and paradigms would help mollify potential environmental problems/constraints in such cities. Besides their significance in solving environmental problems, smart cities also aim to improve citizen&apos;s quality of life and efficiency of public services by optimizing the costs and resources involved. Smart city applications are founded on various enabling technologies and processes related to communication and networking, real-time control and big data analytics, to name a few. These constituents of a smart city infrastructure need to be integrated appropriately and seamlessly for provisioning efficient services to citizens. Each enabling technology has its own unique properties. The interplay and interaction among smart city constituents and their unique properties raise unique security and privacy challenges. In this chapter, we overview enabling and emerging technologies and security and privacy challenges for smart cities. Ensuring authorized accesses to these constituents is critical for ensuring the security of smart cities. We present existing solutions to these challenges mainly from the perspective of access control (AC) with a special focus on risk management, trust, insider threats and secure interoperation. Finally, we present future research directions.
FacetE

Today's natural language processing and information retrieval systems heavily depend on word embedding techniques to represent text values. However, given a specific task deciding for a word embedding dataset is not trivial. Current word embedding evaluation methods mostly provide only a one-dimensional quality measure, which does not express how knowledge from different domains is represented in the word embedding models. To overcome this limitation, we provide a new evaluation data set called FacetE derived from 125M Web tables, enabling domain-sensitive evaluation. We show that FacetE can effectively be used to evaluate word embedding models. The evaluation of common general-purpose word embedding models suggests that there is currently no best word embedding for every domain.
Neural-network-based optimization and analysis for nonlinear stochastic systems

Artificial neural networks (ANNs) are known to be sensitive to the initial setting of parameters and the network architecture, such as the number of hidden nodes in multilayer perceptron (MLP). In this paper, we focus on a network structure which can help to find the proper number of hidden nodes in MLP. In this structure, so called Multilayer Perceptrons with Hidden Multipliers (MLPHM), each of the hidden nodes is associated with a tunable “gate” multiplier. With a specific regularization term, each gate tends to be opened or closed completely at the end of the training, and finally a pruned network is obtained. To study the fault tolerance and to improve the generalization of MLPHM, a noise-injected training scheme is proposed, with both multiplicative noise and additive noise taken into consideration. The objective functions and convergence theorems of the noise-injected training algorithms are obtained, and the latter have been verified by simulations. Applications to several UCI datasets have demonstrated that the proposed algorithms have efficient pruning ability and superior generalization ability.
A skeleton pruning method based on saliency sorting

The pruning of spurious skeletons is an important issue when apply skeletons for object analysis and recognition. Most existing pruning methods use threshold-like parameter to control the effects of pruning. Such approaches usually encounter difficulties in parameter tuning and the quality of pruning. A skeleton pruning method is proposed in this work. This method decomposes a skeleton into a number of skeletal components (SCs), and the terminal SCs are removed one by one according to a saliency measure. SCs may be merged into a new one after each removal. The removal process continues until the desirable number of terminals are achieved. The method can essentially be regarded as the sorting of the terminals, and the truncation of the terminals at the given number. This method is suitable for applications where the desired number of terminals can be determined a priori. Experimental results show that the method can effectively prune the skeletons from the test image bases, and it has the advantage that the terminal number parameter is very intuitive and easy to set for the applications under concern.
3D Printers in the Classroom

Most teachers have not dealt with digital fabrication tools (3D printers, CNC machines, laser cutters, etc.) before encountering them in a school makerspace. Marketing videos usually show a time lapse of an object magically appearing on a build platform. The reality, though, is different. A modest print can take hours or even days, depending on the printer, the material, and the geometry of the print.
La scienza non sarà più la stessa. Più condivisione, cooperazione e solidarietà dopo il Covid-19?

We live in times of bewilderment and confusion. At times like these, it is not unusual to meet opinions about the future. It is perhaps an escape from the uncertainty of the present that grips us. Some voices have argued that humanity after the CoViD-19 pandemic will never be the same again. Optimists say that we will be better. The pessimists predict that we will repeat the mistakes of the past, aggravating them and opening the doors to a new threat for us, for the other species and for the planet. What about science?
Nonlinearism: The Paradigm That Replaced Postmodernism. On the Materials of Song Poetry and Cyberliterature

Abstract This article is an attempt to delineate a new paradigm in the literary arts (including print literature and song poetry). According to the author’s hypothesis, this paradigm cannot be attributed simply to the onset of “digital culture.” The primary reason for the emergence of the new paradigm is the transition from the modernist-postmodernist text to the non-linear text. The transition began in print literature, continued in song poetry, and found its ultimate expression in cyberliterature. The second reason was a change in the artistic paradigm. According to Roland Barthes, in literature, the era of authorial intent (with the author’s mind as the focus) had given way to the period of textuality before reception (which focused on consciousness) became dominant. In this article, the author hypothesizes that at the end of the twentieth century the active postmodernist reception of literary texts was replaced by interactive nonlinear reception.
Discussion of “Experiences with big data: Accounts from a data scientist’s perspective”

The authors have produced an excellent essay with many interesting examples based on their broad experience of data analytics. One emerging issue is the fashion for companies, especially SMEs, to employ a token or trophy data scientist; this practice can lead to a dangerous watering down of data science. Descriptive analysis of big data can lead to insight, however when corresponding end results are known, the analysis can be much richer and lead to significant impact.
Contrastive Learning for Debiased Candidate Generation at Scale.

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning is essential to large-scale recommender systems. Standard approaches approximate maximum likelihood estimation (MLE) through sampling for better scalability and address the problem of DCG in a way similar to language modeling. However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, implying that (1) MLE will preserve and even exacerbate the exposure bias in the long run in order to faithfully fit the observed samples, and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the unfairly ignored items. In this paper, we introduce CLRec, a Contrastive Learning paradigm that has been successfully deployed in a real-world massive recommender system, to alleviate exposure bias in DCG. We theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring, which provides a new perspective on the effectiveness of contrastive learning. We further employ a fixed-size queue to store the items' representations computed in previously processed batches, and use the queue to serve as an effective sampler of negative examples. This queue-based design provides great efficiency in incorporating rich features of the thousand negative items per batch thanks to computation reuse. Extensive offline analyses and four-month online A/B tests in Mobile Taobao demonstrate substantial improvement, including a dramatic reduction in the Matthew effect.
Elastic Cloud Logs Traces, Storing and Replaying for Deep Machine Learning

Storing logs and retrieving it in cloud computing environment is a critical task for Deep Machine Learning models. Time to load large sizes of logs and pre-processing them (prepare logs to be used in learning phase) is a significant issue to machine learning modules that influence training and decision phases. Storing sampled logs in an optimal way that consumes minimum desk storage with accurate and fast replay will give any cloud management system an advantage to start processing logs immediately. Using a practical dimensional reduction method to reduce logs sizes and recover them will improve cloud management system in taking right scaling action and will reduce deep machine learning time complexity. There are many methods in literature for feature extraction and dimensional reduction methods such as Principle Components Analysis (PCA) and wavelet transform. These two methods have a good reputation in processing data set for different goals in machine learning methods like image recognition, signal processing and prediction. In this work, an analysis and comparison is made between PCA and wavelet as methods of cloud logs (considered as data set) dimensional reduction and compression to reduce logs size and logs recover time in order to be replayed in a faster way. This will reduce logs processing complexity, logs foot print and increase decision accuracy by focusing on logs main feature in cloud data center management system.
Mind Your Social Media Manners: Pseudonymity, Imaginary Audience, and Incivility on Facebook vs. YouTube

A quantitative social media content analysis was conducted to examine the role of anonymity, imagined audience and impression management, and incivility in the context of a controversial Eurovision Song Contest win. User comments to posts featuring the artist’s winning performance from Facebook and YouTube were culled and analyzed. Although comments about the singer’s performance were predominantly positive on both platforms, non-performance-related comments were far more negative on Facebook than on YouTube. Though valence of the replies targeting other social media users and the Eurovision organization ranged from neutral to negative, as predicted by notions of imagined audience and impression management, YouTube comments were more negative than Facebook comments toward other users. YouTube comments also contained more profanity than Facebook comments. In terms of interacting with other users, YouTube comments more frequently targeted other users and used more profane language in doing so. Overall, the results suggest that anonymity may not necessarily promote negative commenting behaviors; however, the desire for impression management triggered by the nature of imaginary audience could influence social media user interactions.
Study and Analysis of Digital Watermarking for Medical Images

In the recent years, Internet users are increased tremendously. Hence, it demands a secure communication for different Internet services. Data sharing such as audio, video, image and documents are an integral part, which needs to be secure during transmission. In healthcare applications, the patient's information requires security and integrity during transfer of data from one place to another. The watermarking techniques can improve data protection and content enrichment of medical images. In this article, the literature study is carried out to show the frequency domain techniques are more robust than spatial domain. Apart from this, a brief and comparative analysis of watermarking technique with different image size is presented to show the trade-off between capacity and perceptibility of the image.
Language and Culture in Multicultural Society of English Language Course

This research tried to fulfill the gap in exploring how international tutors learn about the Indonesian language as well as to examine their personal perspectives on the influence of Indonesian culture towards its language. The qualitative descriptive method used as the research design since, in the analysis of the data, someone cannot truly learn about certain languages without learning about its culture and its society. Learning, knowing, and understanding about the local culture means that someone can practically easier teach language to its society. Overall, all international tutors feel fascinated by how Indonesian people think, speak, and act, which influenced by the culture of its society. They also conclude that Indonesian people are expressing and using their language based on their experience with their own culture.
Adiabatic quantum estimation: A numerical study of the Heisenberg XX model with antisymmetric exchange

In this paper, we address the adiabatic technique for quantum estimation of the azimuthal orientation of a magnetic field. Exactly solving a model consisting of a two-qubit system, where one of which is driven by a static magnetic field while the other is coupled with the magnetic field rotating adiabatically, we obtain the analytical expression of the quantum Fisher information (QFI). We investigate how the two-qubit system can be used to probe the azimuthal direction of the field and analyze the roles of the intensities of the magnetic fields, Dzyaloshinskii–Moriya (DM) interaction, spin–spin coupling coefficient, and the polar orientation of the rotating field on the precision of the estimation. In particular, it is illustrated that the QFI trapping or saturation may occur if the qubit is subjected to a strong rotating field. Moreover, we discuss how the azimuthal direction of the rotating field can be estimated using only the qubit not affected by that field and investigate the conditions under which this strategy is more efficient than use of the qubit locally interacting with the adiabatically rotating field. Interestingly, in the one-qubit scenario, it was found that when the rotating field is weak, the best estimation is achieved by subjecting the probe to a static magnetic field.