Reviewerly/exHarmony huggingface dataset:
AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks

The post-COVID-19 era will create major financial losses in organizational resources as a result of fraudulent activities by malicious agents existing at the edge and cloud domains. Most transactional systems from the edge-to-cloud layers lack the robust platform integration (such as application program interface (API) microservices) needed for fraud mitigation in networks and systems. This paper presents an AI containerization API system based on JAVA-SQL container (JSR-233) for fraud prediction and prevention in telecommunication networks. Pipeline modeling involving the Bayesian software implementation paradigm is introduced using the AI-JasCon model. A demonstration of how the AI engine works with a complex network system for observation of some calls, call frequency, and hidden activities for predictive classification (analytics) at the network backend is discussed. Robust network architecture is introduced for deterministic data mining while creating Bayesian computation to determine fraud potentials through prior, posterior, and joint probability distributions. AI-JasCon framework achieves predictive fraud detection with containerization and modularization using class models and data structures. At the network core layer, an enterprise management backend uses linear discriminant via fog controllers that processes identified fraud subscribers in the network. Also, a standard Java middleware container for distributed transaction management, directory services, and messaging is used to test the application. AI-JasCon framework provides a successful standard for determining fraudulent interactions in edge-to-cloud networks while providing a pipeline application programming model for continuous integration and continuous delivery (CI/CD).
Automatic body region localization in 3D-CT images based on the improved YOLO model

Automatic body region localization in medical three-dimensional (3D)-CT images is a critical step of computerized body-wide Automatic Anatomy Recognition (AAR) system, which can be applied for radiotherapy planning and interest slices retrieving. Currently, the complex internal structure of human body and time consuming computation are the main challenges for the localization. Therefore, this paper introduces and improves the YOLO-v3 model into the body region localization for these problems. First, seven categories of body regions in a CT volume image I are defined based on the modification version of our previous work. Second, an improved YOLO-v3 model is trained to classify each axial slice into one of the seven categories. Then, the effectiveness of the proposed method is evaluated on 3D-CT images that collected from 220 subjects. The experimental results demonstrate that the slice localizing error is less than 3 NoS (Number of slices), which is competitive to the state-of-the-art methods. Beyond this, our method is simple and computationally efficient owing to its less training time, and the average computational time for localizing a volume CT images is about 3 second, which shows potential for a further application.
The Impact of External Factors on Stock Return Volatility in the European Banking Sector

Purpose: The main aim of the paper is to examine the impact of external determinants on the banking stock return volatility to evaluate it in terms of the stock market capitalization. Design/methodology/approach: The research was conducted on 182 banks from 26 countries. The sample selected for the study includes all European banks listed on the stock exchange. Quarterly data from the period between 2004 and 2016 was used; it was collected and compiled over a period of 2 years. The research method applied was the panel data model with fixed effects (with or without a robust estimator) and random effects. Findings: Determinants that have a major and statistically significant impact on the analyzed dependent variables are: the unemployment rate, the real interest rate, the beta in Sharpe’s Single-Index Model and the implied volatility of the S&amp;P 500 index and the EURO STOXX50 index. Research limitations/implications: Insights about the strength and direction of influence of these variables on stock return volatility are a valuable addition to the existing body of knowledge that investors resort to when making decisions relating to the capital market. Limitations: The main limitation of this study lies in the fact that the results of the analysis apply solely to the banking sector. Originality/value: Insights about the strength and direction of influence of these variables on stock return volatility are a valuable addition to the existing body of knowledge that investors resort to when making decisions relating to the capital market.
QR Code: Challenges and Potentials for Services in Pos-Covid Era

The choice of this work arises from the interest in launching innovative services to target people.We sought to integrate a technology that could bring more innovation to a product/ service, namely due to mobile trends.Among some options considered, we chose the QR code technology.With the help of a strategic plan, we evaluate whether there is a market and propensity for its widespread use in Portugal.Then, an interactive prototype of the supporting platform is presented for validation by potential users.Most participants consider the idea interesting and promising.They suggested add-ons such as: more video functions and a greater variety of events and resources to apply it.Given the challenges in society, we forecast its application to the quick request for specialized information and/or resources by critical activities such as in health, tourism, etc.
Interactive methods of teaching mathematics, contributing to the formation of competencies in students of the field of training Agronomy format

The article outlines aspects of the formation of general professional competencies (GPC-1) through the study of mathematical disciplines; reveals the features of the methodology for the application of tasks (tasks of profile content, task case, task-situation), which are the main means of assessing the formation of the corresponding indicators of achievement of competence: ID-1, ID-2, ID-3, etc. The work analyzed educational publications in mathematical disciplines for the presence of tasks for an objective assessment of the level of formation of general professional competence (GPC-1), the problem of developing the content of criteria and assessment tools for formation of general professional competencies. The authors provide examples of tasks in the disciplines of Higher Mathematics and Mathematics and Mathematical Statistics, designed to form skills in identifying and defining a problem, choosing research methods, analyzing, interpreting the result and transferring fundamental knowledge into the language of professional activity.
An Efficient Method of Supervised Contrastive Learning for Natural Language Understanding

Fine-tuning on the pre-trained language models has achieved state-of-the-art performance in many natural language understanding (NLU) tasks. For classification tasks, the cross-entropy loss is commonly employed as optimized objective in the fine-tuning stage. However, models fine-tuned with this method can not gather the contrastive information between positive and negative samples. The data points encoded by these models are close to decision boundary. In this paper, we propose a supervised contrastive learning (SCL) method to better fine-tune in text classification and regression tasks. The proposed SCL loss obtains a large margin. In addition, we explore 6 text augmentation strategies to generate hard positives and negatives at the embedding layer. The ELECTRA-Base and ELECTRA-Large fine-tuned with our method surpass the MPNet and DeBERTa-Large on the GLUE validation set, respectively. Experimental results show that this method leads to better generalizability, with 10.1 points improvement on 3 datasets of GLUE in zero-shot settings. The code is available at: https://github.com/littlewhite-gao/SCL.
Information Sharing as a Human Right

In the Norwegian view, protection of human rights has paramount importance, and among these rights, as Asbjorn Eide notes, participation stands foremast. One object of the human rights movement as described by another Norwegian offers particular hope for building peace in a wartorn world — participation or information sharing. The tautology is that both crime and punishment are exercises of power, so that being more powerful practically by definition means having greater capacity to get away with crime and resist punishment. In criminology, Nils Christie stresses that attaining intimate knowledge of others and recognizing mutual dependence in participatory structures are key conditions for peaceful community life. Given the fact that Norwegians are relatively inclined to peace and disinclined to punishment and violence, we might do well to take special note of the central place participatory decisionmaking occupies in Norwegian thinking.
The Four-Category Ontology Modulo Topological Ontology1

In this chapter I refer to the four categories proposed by E. J. Lowe in The Four-Category Ontology. These categories will be discussed as part of the so-called topological ontology. I will propose, among other things, a way of understanding ideas and their hierarchy, as well as a way of understanding objects (individuals) and attributes (properties). From the point of view of topological ontology, tropes are a problem. Perhaps topological ontology cannot describe this phenomenon. The main aim of the work is to show that by applying tools of general topology, the basic concepts of Lowe’s four-category ontology can be presented (or modelled) formally and fully accurate. In particular, the theorems in Section 2.4 indicate the topological properties of the ideas and their topological relationships with objects, while conditions 1)–4) given in Section 3 model the relationships between kinds, attributes, objects and tropes in topological (and set-theoretical) way.
API as a way to interact with social networks

In the realities of the pandemic, chat bots have become indispensable helpers. They do not need a lot of resources and constant human control. A method of interacting with social networks through a specialized software interface Web API, which is the basis of the REST architecture, is considered. The basic structure of requests for receiving and sending data to servers is presented. On the example of the implementation of a chatbot for vk.com, capable of automating a dialogue with users, the main design stages are presented, including the requirements for the implementation and operation mode based on the client-server architecture, implementation and testing. The project server is implemented on a Raspberry Pi4 single-board computer. Demonstrated code for performing basic queries and implemented a Long Polling approach to continuously track and distribute user messages. Methods were formed to obtain the necessary resources from the server, to declare a new resource on the server, to update information on the server, and to delete certain objects from the database. The result was a patented software product "Intelligent assistant of VSUIT for social networks".
Introduction

More than a century after the advent of intellectual property (IP) laws in Nigeria, IP has emerged from a relatively obscure subject matter into a notable field of law that has generated considerable debates across scholarship, policy and practice spheres. Yet it is ironic for an increasingly dynamic legal field like IP to hold such an unenviable pedigree of one of the least revised or improved pieces of legislation in the gazettes of Nigerian law. While the pioneer set of IP laws, comprising trademark, patent and copyright acts, were reputed to have served imperial interests, subsequent replacements, with the emergence of local industry, did not make much impact in the legal and commercial environment. That trend neither has significantly changed nor has come into full circle. In recent years, the growing significance of IP has been a subject of study in its doctrinal, empirical and interdisciplinary contexts, affirming that IP has truly emerged not only as a major currency of the digital age but also as indispensable to national development in our country and region. Policymakers, legal industry, academics, development experts and the public have risen to narratives of the strategic role of IP in emergent digital economy, and accompanying consensus on IP reform has resonated across board. This book reinforces that narrative and brings together contributions from Nigerian IP scholars who understand the nuances of the subject and application of its principles in national contexts. The array of authors in a single volume is provided by the idea to produce a collection of essays in honor of Professor Adebambo Adewopo, who is one of the fathers of IP teaching and scholarship and has contributed to the development of IP law and policy in Nigeria.
Simulating Fear as Anticipation of Temporal Differences: An experimental investigation

Humans use emotional expressions to communicate appraisals. Humans also use emotions in evaluating how they are doing compared to their current goals and desires. The Temporal Difference Reinforcement Learning (TDRL) Theory of Emotion proposes a structure for agents to simulate appropriate emotions during the learning process. In previous work, simulations have shown to reproduce plausible emotion dynamics. In this paper we examine the plausibility and intepretability of TDRL-simulated fear, when expressed by the agent. We presented different TDRL-based fear simulation methods to participants <tex>${\left(n=237\right)}$</tex> in an online study. Each method used a different action selection protocol for the agent&#x0027;s model-based anticipation process. Results suggest that an <tex>${\in}$</tex>-greedy fear policy <tex>${\left(\in=0.1\right)}$</tex> combined with a long anticipation horizon provides a plausible fear estimation. This is, to our knowledge, the first experimental evidence detailing some of the predictions of the TDRL Theory of Emotion. Our results are of interest to the design of agent learning methods that are transparent to the user.
Physics News

Physik in unserer ZeitVolume 50, Issue 5 p. 218-218 Treffpunkt Forschung Physics News First published: 02 September 2019 https://doi.org/10.1002/piuz.201970508Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume50, Issue5September 2019Pages 218-218 RelatedInformation
Reinforcement learning in Connect 4 Game

Reinforcement learning allows a machine or software agent to learn its behaviour based on the response of the environment. It permits machine and software package agents to mechanically confirm the perfect behaviour in a very explicit context to maximise its performance. The distinction in reinforcement learning for supervised learning is that only partial feedback is given regarding the learner’s predictions. Beside, predictions can have long-term effects by affecting the future state of the controlled system. Thus, time plays a special role. The goal of reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithm’s qualifications and limitations. It is very interesting to learn reinforcement learning from a large number of useful practical applications from artificial intelligence problems to control engineering. In this project, we focus on those algorithms of reinforcement learning. Scaling the project looks at challenges for reinforcement learning in Connect4 game, together with a review of proposed solution methods. While this list has a game-centric approach and some items are specific to the game, a large part of this overview may also provide an understanding of other types of applications.
Survival Book to Help BIPA Learners Off the Hook of Culture Shock

From time to time, Indonesian for Foreign Speakers (BIPA) continues to grow, including BIPA at the State University of Surabaya (Unesa). It cannot be denied that in its journey, BIPA Unesa encountered various problems. One of them is a report from a foreign student who was confused when he first arrived in Surabaya. Their confusion includes finding the ideal place to live, using online motorcycle taxi applications, and using public facilities such as markets, hospitals, and police stations. Not only that, but the understanding of academic rules on campus has also not been able to be conveyed on target. Some local students and lecturers have helped, but the limited number and time have made it impossible to assist students. Therefore, this research focuses on making a pocketbook called "survival book" in five different languages ​​for foreign students. The benefits of this research are expected to be able to solve the concerns of BIPA students when they first come to Surabaya with the development of a pocketbook. This research belongs to the category of product development. With this research, it is hoped that it will produce an output in the form of a survival book for BIPA Unesa students as well as scientific articles that will be published in accredited national journals.
API Recommendation Based on WII-WMD

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.
Analysing knowledge in social big data

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, the semantic web, and social networks. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation and visualising data. In this paper, we will present a new approach that can extract entities and their relationships from social big data, allowing for the inference of new meaningful knowledge. This approach is a hybrid approach of multi-agent systems and K-means algorithm.
EDITOR OF THE “RUSSIA” MAGAZINE. BIOGRAPHY OF I. G. LEZHNEV – ARCHIVAL DOCUMENTS AND PUBLICATIONS. PART 3

The article considers the archival materials and the printed resources related to the major milestones of the professional biography of I.G. Lezhnev, the writer and editor of the “Russia” magazine. The ideological work of I.G. Lezhnev in the first part of the 1920s connected with the issuing of “Russia”, the magazine which belonged to the “Smena Vekh” ideological project, can be understood through his publications in this magazine, his letters to N.V. Ustryalov published by M.S. Agurskiy as well as through the resources in the Russian archives. The sequence of events that has ended up in the closure of the “Russia” magazine and the case of deporting its editor I. G. Lezhnev “beyond the USSR” are revealed in the documents stored in the funds of the Russian State Archives of the Social and Political History and the Central Archives of the Federal Security Service of the Russian Federation. Having researched the information in the documents of the I.G. Lezhnev fund in the Russian State Archives of Literature and Art, the author of the present article analyses the professional duties of I.G. Lezhnev – the staff member of the Trade Mission of the USSR in Berlin. The article provides an overview of the resources connected with I.G. Lezhnev’s return from Germany to the USSR, and of the essential role of his “extended membership application” for joining the Party, of his autobiographical and repenting “The Notes of a Contemporary” which in many ways became life-defining for his author; attention was also drawn to his work in the “Pravda” newspaper and other milestones of his life. For the first time, the documents from private collections have been perused – those detailed materials pertaining to the publication in the USSR of Lezhnev’s book “The Notes of a Contemporary”.
A new approach to prevent the decrease of population diversity in genetic algorithms

A genetic algorithm is a population based evolutionary algorithm inspired from natural evolution. A major drawback of this algorithm is the decrease of population diversity throughout execution. Several methods are used to prevent this drawback, including island model approach and diffusion model approach. In this study, a new approach (Earth Model) is proposed in order to slow down this decrease of population diversity. In this model, population is distributed randomly over a unit sphere and mating is constrained with a critical spherical distance. The model is tested on Euclidian Travelling Salesman Problem. Initial tests show that the decrease in diversity is slower in Earth Model and total distance of the best solution of the test problem is approximately 4.8% lower than traditional genetic algorithm.
Online Teaching Platform in Vocational College English Education

In the “Internet + ” era, the English majors in higher vocational education in China are also undergoing a massive transformation. Teachers have increasing opportunities to use the online teaching platform. And they also need to probe into how to play the role of the online education platform, according to the actual teaching situation, circumvent the shortcomings of the online education platform, and improve students’ practical ability. This paper mainly analyzes the pros and cons of the online education platform in the application of higher vocational English education and tries to find out measures to play the role of online education platform hoping to provide reference for teachers.
Low Latency Multi - Dimensional Offloading in Intelligent Transportation System

Artificial intelligence will play a vital role for autonomous and cooperative driving in future intelligent transportation systems (ITS). Whereas, it needs sufficient computing resource, data resource and elaborated models to provide powerful artificial intelligence for enhanced driving and cooperating in ITS. Current schemes, such as computation offloading, distributed data caching, and remote model training, usually focus on part of this problem, and cannot substantially solve the multidimensional burden in such intelligent system. In this paper, we propose a multi-dimensional offloading (MDO) scheme to realize efficient and joint offloading of computation, data cache and decision model in ITS. Specifically, an optimization model of MDO is presented and divided into two sub-problems to get the optimized resource allocation with extremely low latency, which is crucial for a time-sensitive system. Simulations are conducted to verify the performance of our proposed scheme, and the simulation results show MDO can decrease the time latency considerably compared with traditional schemes.