Reviewerly/exHarmony huggingface dataset:
Evaluating the Robustness of Collaborative Agents

In order for agents trained by deep reinforcement learning to work alongside humans in realistic settings, we will need to ensure that the agents are \emph{robust}. Since the real world is very diverse, and human behavior often changes in response to agent deployment, the agent will likely encounter novel situations that have never been seen during training. This results in an evaluation challenge: if we cannot rely on the average training or validation reward as a metric, then how can we effectively evaluate robustness? We take inspiration from the practice of \emph{unit testing} in software engineering. Specifically, we suggest that when designing AI agents that collaborate with humans, designers should search for potential edge cases in \emph{possible partner behavior} and \emph{possible states encountered}, and write tests which check that the behavior of the agent in these edge cases is reasonable. We apply this methodology to build a suite of unit tests for the Overcooked-AI environment, and use this test suite to evaluate three proposals for improving robustness. We find that the test suite provides significant insight into the effects of these proposals that were generally not revealed by looking solely at the average validation reward.
Definition of a Digital Tool to Create Physical Artifacts: The Case of the Gamers4Nature Project

Abstract In a context where the urgency to act towards the promotion of environmental awareness is imperative, the Gamers4Nature (G4N) project aims to encourage the active participation of students in the creation of mobile games addressing environmental-related themes. This chapter introduces the G4N Cards Set Canvas, a tool integrated into the G4N portal-repository. The G4N Cards Set Canvas is a digital tool that allows the design of environmental-related thematic cards and has two main goals: (i) to provide the G4N project with a tool to ease the creation of the thematic cards for the Toolkit to Game Design by environmental organizations and/or teachers; (ii) to be used by users with no experience in the design field. This tool will be developed following a User-Centered Design approach, with iterative and systematic reviews of the tool, and it will emphasize the connection between the physical (i.e. Toolkit) and digital (i.e. portal-repository) artifacts. A snowball literature review and an overview of design and presentation tools and platforms currently available did not reveal the existence of suitable platforms to create graphical design contents while complying with a brand’s standard’s manual for non-experienced users in the design field. Thus, an opportunity for novelty arises from this gap, adding a layer of concern with designing a usable and accessible tool for the end-users.
Decentralized Bottom-Up Energy Trading Using Ethereum As a Platform

In the past decade, there has been a significant increase in distributed energy resources. This transformation has rendered the grid more bidirectional and transformed many small consumers into prosumers. However, these small power producers are not able to optimize their revenue since, currently, they can only sell to local energy suppliers or opt to sell at a fixed feed-in tariff. If intermediaries are eliminated from energy trading, both individual buyers and prosumers can increase their profitability. Blockchain technology could facilitate this scenario. In this paper, the simulated environment of a hierarchical energy trading market using Ethereum’s smart-contract technology is created as a proof-of-concept of using blockchain technology in energy trading. A dynamic grid fee based on electrical network loading is calculated to demonstrate an economic incentive for agents to have flexible load demand as well as to promote local resource utilization. The price volatility of cryptocurrency is addressed by designing a specific energy token for the model.
Using Associative Classification for Detecting E-Banking Phishing

Phishing attack has become very common in recent years especially in the financial application setting.A lot of losses have been recorded as a result of this attack from users and subscribers all over the globe.The research community has also been very concerned about this development with leaves an unsavoury aftermath on its victims, hence, several models, systems, architecture and frameworks have been developed by the researcher in an attempt to tackle this menace.The key limitations of these developments include lack of standard classification, low model efficiency and performance in terms of speed and time and high cost of developing the models.In this work, we have developed an enhanced Phishing detection system using Associative Classification technique.The Structured System Analysis and Design Methodology (SSADM) was adopted in this approach.The system was implemented using Hypertext Preprocessor (PHP) and MySQL as database.Form our results the proposed model had an overall accuracy score of 86.6% which outperformed the existing system with 55.9% when evaluated using selected parameters.This system could be beneficial to Nigerian Banks, to Digital Banking Application Users and to the entire research community.
Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions (Student Abstract)

AI-systems that model and interact with their users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI-system, they may actually hurt the performance with respect to individual users. Prior work has studied the tradeoff between improving the system's performance following an update and the compatibility of the updated system with prior user experience. The more the model is forced to be compatible with a prior version, the higher loss in performance it will incur. This paper shows that that by personalizing the loss function to specific users, it is possible to increase the prediction performance of the AI-system while sacrificing less compatibility for these users following an update to improve the system's performance. Our approach updates the sample weights to reflect their contribution to the compatibility of the model for a particular user following the update. We construct a portfolio of different models that vary in how they personalize the loss function for a target user and then select the best model to use based on a validation set. We use a model selection algorithm to choose the best model from the portfolio for each user given a set of features that reflect the users' characteristics and performance of the different models on a training set. We apply this approach to three supervised learning tasks commonly used in the human-computer decision-making literature. We show that using our approach leads to significant improvements in the performance-compatibility tradeoff over the non-personalized approach of Bansal et al., achieving up to 300% improvement for certain users.
ANALYSIS OF POTENTIAL SECURITY ISSUES IN REGIONAL GOVERNMENT X WEBSITE USING SCANNING METHOD IN KALI LINUX

The importance of website security is a top priority after data leakage or damage occurs. Website is a web page that is interconnected and contains a collection of information and can be accessed through the home page using a browser and internet network. According to the ministry of communications and information, 50% of the government's website is under threat from hacker attacks that can harm private information. Vulnerability assessment or process of identifying the weaknesses of a system can be an effective way to control and prevention against risks that occur. Given these problems, it is necessary to analyze the potential vulnerabilities on websites with vulnerability assessments aimed at preventing security vulnerabilities. In this study the analysis of potential website security loopholes was performed using scanning methods. The test is carried out with a vulnerability assessment using tools that are available on Linux and run on a virtual machine. Kali Linux is an operating system that has many tools including penetration testing, ethical hacking and network security assessment. This research was conducted using uniscan and nmap tools by scanning the target URL and assisted by using a web browser. The result of testing is to find a security vulnerability using a scanning method with tools and then giving the solution of the vulnerability it acquired.
Detecting Malicious URLs using R-CNN and Cloud

In today’s information age as use of websites, mobile apps and all forms of information sharing forms have increased which gave rise to malicious URL forms. These malicious URLs are forwarded and users attention is diverted from the main course for what he is searching to other non-necessary and harmful content, thus wasting a lot of time and money. Theses malicious URLs have given rise to authentication thefts, money thefts and bullying of a user who falls in to a trap set by hackers by accessing these URLs. To resolve and find a solution to this kind of menace there is need to detect and prevent users from accessing these URLs. So, while studying various techniques put forward by various authors in different research papers, we found a few techniques quite interesting and useful. The first is detecting malicious URLs using CNN and GRU. The second is where a text mining technique is proposed using Natural Language Processing (NLP) which can be used for classification. The third is a combination of CNN and NLP. By studying them we came to understand that there should be a combination of both NLP and CNN together to implement a successful malicious URL detection system. So, in our paper we are proposing a fusion of R-CNN, NLP and Cloud together. The main work in our paper is to collect malicious and healthy URL which will be done using internet and multiple sources and combined as one dataset. Thus, we will use Google cloud to create a blacklisted URL database of our own and not depend upon multiple sources internet for them. In our system first we will create a blacklist database on cloud and then apply classification on it using NLP and machine learning algorithm SVM. The second step will be to use same URL dataset to train a R-CNN AI algorithm and get an output in form of malicious identified URLs. Then in the final phase we will compare the final results from SVM and R-CNN and analyse which one is efficient and highs and lows of the technique.
Bootstrapping Dialog Models from Human to Human Conversation Logs

State-of-the-art commercial dialog platforms provide powerful tools to build a conversational agent. These platforms provide complete control to the dialog designer to model user-agent interactions. However, a dialog designer needs to rely on domain experts to manually build the dialog model -- by creating dialog flow nodes and modeling user intents. This process is laborious, time consuming and expensive and does not allow the designer to exploit human to human conversation logs effectively. In this work, we present a research prototype that can ingest human-to-human conversation logs between an end-user and an agent, and suggest user-intents and agent-responses, given a conversation context. We utilize human to human conversation logs to build two emulators: user and agent. An agent emulator models an agent response given the conversation context so far, and a user emulator outputs possible user responses. Our system is able to recommend conversational intents as well as conversation flow using emulators based on real-world data, thus making the process of designing a bot more efficient. To the best our knowledge this is the first system that enables data-driven dialog model creation by emulating users and agents.
READINESS OF TEACHERS TO INTRODUCE REMOTE EDUCATIONAL TECHNOLOGIES IN HIGHER EDUCATION

The article discusses the problem of training university teachers to implement distance-learning technologies in higher professional education. Distance learning technologies have great potential in improving the quality and accessibility of vocational education. The relevance of distance education technologies (DЕT) in Kazakhstan has gained particular importance in connection with the suspension of distance learning from January 1, 2019. The effectiveness of DЕT depends on the degree of implementation of regulatory support, infrastructure, software, digital educational content, but the main factor is the willingness of the faculty to use them. Teachers should know the essence and basic principles of the use of DЕT in higher education, modern platforms for the use of DЕT; вe able to create own online courses and other digital educational resources.
Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.
P-SGD: A Stochastic Gradient Descent Solution for Privacy-Preserving During Protection Transitions

Advances in privacy-enhancing technologies, such as context-aware and personalized privacy models, have paved the way for successful management of the data utility-privacy trade-off. However, significantly lowering the level of data protection when balancing utility-privacy to meet the individual’s needs makes subsequent protected data more precise. This increases the adversary’s ability to reveal the real values of the previous correlated data that needed more protection, making existing privacy models vulnerable to inference attacks. To overcome this problem, we propose in this paper a stochastic gradient descent solution for privacy-preserving during protection transitions, denoted P-SGD. The goal of this solution is to minimize the precision gap between sequential data when downshifting the protection by the privacy model. P-SGD intervenes at the protection descent phase and performs an iterative process that measures data dependencies, and gradually reduces protection accordingly until the desired protection level is reached. It considers also possible changes in protection functions and studies their impact on the protection descent rate. We validated our proposal and evaluated its performance. The results show that P-SGD is fast, scalable, and maintains low computational and storage complexity.
A Novel Video Watermarking Algorithm using DTCWT

In this paper a new novel color video watermarking algorithm has been proposed using Dual Tree Complex Wavelet Transform (DTCWT). Digital watermarking is an emerging technology for copyright protection for digital multimedia data. Now-a-days a lot of digital data are exchanged in the internet, so to protect the digital multimedia data, digital watermarking is used, the data may be an image or video or an audio data. The main objective of this paper is to maintain perceptivity of digital video and design a robust watermarking algorithm and maintaining the tradeoff between robustness and perceptivity.
Weight distribution of cyclic codes defined by quadratic forms and related curves

We consider cyclic codes C L associated to quadratic trace forms in m variables Q R (x) = Tr q m /q (xR(x)) determined by a family L of q-linearized polynomials R over F q m , and three related codes C L,0 , C L,1 , and C L,2 .We describe the spectra for all these codes when L is an even rank family, in terms of the distribution of ranks of the forms Q R in the family L, and we also compute the complete weight enumerator for C L .In particular, considering the family L = x q , with fixed in N, we give the weight distribution of four parametrized families of cyclic codes C , C ,0 , C ,1 , and C ,2 over Fq with zeros {α -(q +1) }, {1, α -(q +1) }, {α -1 , α -(q +1) }, and {1, α -1 , α -(q +1) } respectively, where q = p s with p prime, α is a generator of F * q m , and m/(m, ) is even.Finally, we give simple necessary and sufficient conditions for Artin-Schreier curves y p -y = xR(x)+βx, p prime, associated to polynomials R ∈ L to be optimal.We then obtain several maximal and minimal such curves in the case L = x p and L = x p , x p 3 .
CORRIGENDUM: Computer system validation

The authors regret that in „Computer system validation: Example of quality management system design and process implementation“ the following co-author was mistakenly missed from the authorship: Tomaž Sallubier, TS4U d.o.o, Kamnik, Slovenia.
Collaborative Feedback Approach for Improving Customer Engagement and Product Increment in Agile Software Development

Customer engagement and feedback are crucial in agile software development to ensure the product released meets their needs. However, lack of involvement and delayed feedback are the main challenges, especially if the customers are in distributed locations. These could cause the product increment quality, poor iteration planning decisions, and late product delivery. A collaborative feedback approach is proposed in this paper to support gathering feedbacks of product increment and decision-making in iteration development planning. The proposed approach also embraces a quality assurance (QA) mechanism to ensure quality adheres to the product increment. An evaluation was conducted where a tool, called CrowFeID, was developed as a proof-of-concept of the proposed approach and to support the iteration review process. As a conclusion from the responses received, the proposed approach and tool can reduce delayed feedback during the iteration review and provides valuable input in making decisions for the product increment and next iteration planning.
Feature selection by a distance measure method of subnormal and non-convex fuzzy sets

Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods.
Local Navigation Application

This paper is aimed towards providing an easy access to locations around the college area especially useful to students moving to the city from elsewhere. Taking into consideration, the wide- spread use of cell-phones the project has been designed in the form of an application. The application created is designed with taking into consideration the immediate needs of the students. The application works in co-ordination with Google Maps and provides location access. The application also provides useful information regarding the locations. This paper provides the summary of the process of creation of this project.
Philosophy of Religion for OCR: The Complete Resource for Component 01 of the New AS and A Level Specification, Dennis Brown and Ann Greggs, Polity Press, 2018 (ISBN 978‐1‐5095‐1798‐5), 240 pp., £17.99

Reviews in Religion & TheologyVolume 28, Issue 2 p. 158-161 Review Philosophy of Religion for OCR: The Complete Resource for Component 01 of the New AS and A Level Specification, Dennis Brown and Ann Greggs, Polity Press, 2018 (ISBN 978-1-5095-1798-5), 240 pp., £17.99 Alessandro Giostra, Alessandro Giostra Deputazione di Storia Patria per le MarcheSearch for more papers by this author Alessandro Giostra, Alessandro Giostra Deputazione di Storia Patria per le MarcheSearch for more papers by this author First published: 29 June 2021 https://doi.org/10.1111/rirt.13974Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume28, Issue2April 2021Pages 158-161 RelatedInformation
Pengaruh Kemampuan Verbal Terhadap Prestasi Belajar Matematika Siswa Kelas VIII SMP Negeri 1 Kapontori

The purpose of this study was to determine the effect of verbal ability on mathematics learning achievement of class VIII students of SMP Negeri 1 Kapontori. This study used an ex-post factorial design. The population of this study were all grade VIII students of SMP Negeri 1 Kapontori in the academic year 2020/2021. The sample was students of class VIII-A 30 and VIII-B 27 who were selected using the proportional random sample technique. The research instrument was a questionnaire and student learning achievement. The results obtained are: 1) The verbal ability of the eighth grade students of SMP Negeri 1 Kapontori has excellent abilities and has high, medium and low verbal skills. The verbal ability of class VIII students of SMP Negeri 1 Kapontori is in the medium category with a percentage of 75.5% this is because verbal skills are very good.
PENGGUNAAN APLIKASI TEGUR SAPA GADGED PADA PEMBELAJARAN MENULIS

This study aims to know the preparation, implementation, and results of the application used in applying gadgets on how to write in grade 4. To overcome the difficulties learners face in writing, the teacher practised a learning model using scolds greet apps, which is a program application that connects voice to text and text to voice. Scolds application gadgets using colour note and greet applications gadget using voice aloud reader. The usage of scolds greet gadget apps can overcome students' difficulties in finding ideas and making easy-to-write concepts. To bring a positive change in the interest of learners in writing. The evidence can be seen from the recapitulation of marks in each meeting increased. The first meeting has an average of 7.83, the second meeting has 11.48, and at the third meeting, it has changed to 13.72. the results of questionnaires and interviews from 34 students only 12 people who like to write essays, after applied scold greet gadgets from 34 students, 28 learners to be like to write essays.