A Collaborative Machine Learning Scheme for Traffic Allocation and Load Balancing for URLLC Service in 5G and Beyond

Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .

Gamified Learning Systems’Personalized Feedback Report Dashboards via Custom Machine Learning Algorithms and Recommendation Systems

Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.

A Two-folded Impact Analysis of Schema Changes on Database Applications

Database applications are becoming increasingly popular, mainly due to the advanced data management facilities that the underlying database management system offers compared against traditional legacy software applications. The interaction, however, of such applications with the database system introduces a number of issues, among which, this paper addresses the impact analysis of the changes performed at the database schema level. Our motivation is to provide the software engineers of database applications with automated methods that facilitate major maintenance tasks, such as source code corrections and regression testing, which should be triggered by the occurrence of such changes. The presented impact analysis is thus two-folded： the impact is analysed in terms of both the affected source code statements and the affected test suites concerning the testing of these applications. To achieve the former objective, a program slicing technique is employed, which is based on an extended version of the program dependency graph. The latter objective requires the analysis of test suites generated for database applications, which is accomplished by employing testing techniques tailored for this type of applications. Utilising both the slicing and the testing techniques enhances program comprehension of database applications, while also supporting the development of a number of practical metrics regarding their maintainability against schema changes. To evaluate the feasibility and effectiveness of the presented techniques and metrics, a software tool, called DATA, has been implemented. The experimental results from its usage on the TPC-C case study are reported and analysed.

Leveraging Digital Entrepreneurship Through Collaboration Between Incumbent Firms and Entrepreneurial Ventures： An Inbound Open Innovation Perspective

Considering that traditional research and development （R＆D） models cannot keep up with the disruptive advances, the incumbent firms should consider external sources for obtaining and commercializing digital innovations. By contrast, entrepreneurial ventures have the necessary tech development skills and agility to create digital innovations. There has never been a better time for incumbent firms and entrepreneurial ventures to collaborate One paradigm that establishes such mutual beneficial partnership is called ＂inbound open innovation＂. Based on a multiple-cases design, this research provides evidence of the benefits, the degree of co-creation required, and the various collaboration schemes that emerge through an open innovation initiative

The accounting valuation of bank stocks in Europe

In this study we examine the validity of the earnings-book value capitalization model in the pricing of bank stocks in Europe. We perform cross-sectional tests using a sample of banks operating in 21 European Union countries. We split our sample into code and common law countries to control for the influence of the financial reporting system upon the pricing of earnings and book value. Our empirical findings suggest that disaggregating total reported clean surplus income into reported gains and losses from securities and other income increases the explanatory power of the accounting valuation model. Indeed, reported gains and losses from securities are priced lower than other income, irrespective of whether the financial reporting system is classified as a code or a common law system, probably because the market considers the former as a less stable source of income. Additionally, we observe that the pricing of both earnings and book value is influenced by the financial reporting system. We conclude that earnings announced under a common law financial reporting system are priced differently from those reported under the respective code law system. Finally, we provide evidence that comprehensive and dirty surplus income are two value relevant variables for banks reporting earnings under the code law system, although dirty surplus income is priced lower than clean surplus income.

Earnings Management in State-Owned Enterprises in a Period of Macroeconomic Instability

The purpose of this paper is to examine whether and to what extend Greek state-owned enterprises engage in earnings management techniques during 2012-2016.Most prior studies on earnings management have used data from large economies with relative stable macroeconomic contexts(e.g.,USA).Our knowledge about earnings management in smaller economies is rather limited.To fill this void,we conducted our study in Greece and focused on the abovementioned period,which is very interesting given that Greece experienced a substantial political and macroeconomic instability(e.g.,four elections,one referendum,capital controls,and two memorandums).Two different models(i.e.,The Earnings Distribution Model and the Modified Jones Model)are used to provide answers to the research questions.The sample is composed of 50 state-owned enterprises.The results show that Greek state-owned enterprises did not adopt any earnings management techniques during the examined period.These results are rather robust given that they come from two different models.According to the literature,macroeconomic and political instability provide incentives to managers to use earnings management techniques.Interestingly though,our results suggest that this is not the case for a smaller economy.The findings of this study will allow investors,auditors,and users of the financial statements to make better decisions when studying companies that are experiencing those difficulties.The adoption of two different methods and the similar results is an indication of more robust and valid findings.

Managerial Ability and Forecast Accuracy

We examine the relation between managerial ability and management forecast accuracy. We base our analysis on S＆P 500 Composite Index constituents for the period of 2006-2012. Data were collected from Thomson Reuteurs, Compustat and Demerjian, Lev, and McVay （2012）. We find that forecast accuracy is positively associated with managerial ability in the case of sales forecasts. Specifically, more able managers are associated with lower magnitude＇s forecast errors in the case of sales forecasts. Additional analysis finds that managerial ability is immaterial to EPS figures＇ forecast accuracy, i.e., EPS forecasts appear not to be affected by manager＇s superiority. Regarding sales forecasts, the results are consistent with the assertion that managers impact the quality of the delivered management forecasts. Regarding EPS forecasts, the results are in alignment with Demerjian, Lev, Lewis, and McVay （2013） who highlighted that managerial ability is an ability score related to the entire management team.

What is the Impact of Financial Difficulties on Reaching Happiness?

This paper provides a framework to reveal a measure of happiness based on British Household Panel Survey.Thereafter we focus on what is the impact of financial difficulties faced by individuals in the survey on reaching the frontier.By doing so,we also explore the underlying causal ity threads between happiness and financial difficulties.Our methodology follows a stochastic frontier analysis framework to estimate the happiness frontier,while we employ a flexible panel VAR for the underlying re sponses to shocks and estimating variance decompositions.

Optimal Management of the Pension Contribution Rate Using the H∞-Control Theory

In this paper, we construct a general model for a pension fund when there are time delays in the valuation process. Actually, we use the standard structure of the basic reserve equation in order to rebuild a more sophisticated approach based on the theory of H∞ control. Our model evaluates the incomplete information from the delayed fund valuations—due to the oscillatory pattern for benefit claims and investment experience of the past years—within the context of uncertainty additionally to the randomness which certainly exists. So, we construct estimations for the optimal proposed contribution rates based on a feedback mechanism which is a robust stabilization controller, using typical linear matrix inequalities. Finally, a numerical application is fully investigated to obtain further insight into the problem.

Opening design using bayesian optimization

Background Opening design is a major consideration in architectural buildings during early structural layout specification.Decisions regarding the geometric characteristics of windows,skylights,hatches,etc.,significantly affect the overall energy efficiency,airflow,and appearance of a building both internally and externally.Methods In this work,we employ a goal-based,illumination-driven approach to opening design using a Bayesian optimization approach based on Gaussian processes.A method that enables designers to conveniently set lighting intentions in conjunction with the qualitative and quantitative characteristics of the desired openings is proposed.Results The parameters are optimized within a cost-minimization framework to calculate geometrically feasible,architecturally admissible,and aesthetically pleasing openings of any desired shape while taking into account the designer′s lighting constraints.

Formulating Innovative Societal Educational Learning Systems Using Gamification Techniques in 3D Environments

Gamified education has set the ground for the delineation of state-of-the-art literacy skills,enabling learners to develop their digital,cognitive,emotional and social competencies,through active experimentation,motivation and engagement,all while ensuring that pedagogical objectives are being effectuated,therefore capacitating the optimization of the learning process,as a whole.In this paper,we commence by assessing some of the most fundamental frameworks,models and theories evolved around the concept of gamification.We,additionally,showcase schemes through which it stimulates the actualization of active,multidimensional learning,by promoting the application of technological advancements,for the enhancement of learners’hard and soft skills,within time and cost effective frameworks.Ultimately,we,thoroughly,present a newly introduced,cross-platform,innovative educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments,howlearn.Using gamification techniques,in 3D virtual environments,for the realization of STEAM related experiments which cover the vast majority of learners’subject material,while,simultaneously functioning as an authoring tool,whilst essentially accounting for accessibility,geographical and other socio-economic considerations,howlearn advocates youth-centered learning,providing the foundations towards the establishment of gamified,socially sustainable,multifaceted,inclusive educational learning systems.

Secure smart contract-based digital twins for the Internet of Things

The proliferation of Internet of Things(IoT)devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased security.We argue that digital twins of IoT devices,with the right design,can enhance the security,reliability,auditability,and interoperability of IoT systems.The salient features of digital twins have made them key elements for the IoT and Industry 4.0.In this paper,we leverage advances in W3C’s Web of Things(WoT)standards and distributed ledger technologies(DLTs)to present a novel design of the smart contract-based digital twins with enhanced security,transparency,interoperability,and reliability.We provide two different variations of that general design using two different blockchains(one public and one private,permissioned blockchain),and we present design trade-offs.Furthermore,we introduce an architecture for accessing and controlling IoT devices securely and reliably,providing full auditability,while at the same time using the proposed digital twins as an indirection mechanism(proxy).The proposed architecture leverages the blockchain to offer notable properties,namely,decentralization,immutability,auditability,non-repudiation,availability,and reliability.Moreover,it introduces mass actuation,easier management of IoT devices,and enhanced security to the IoT gateways,enables new business models,and makes consumer devices(vendor-)agnostic.

The explosion in cryptocurrencies:a black hole analogy

Using an analogy between finance and astrophysics,this study aims to investigate whether there exists a mechanism that can describe the explosive increase in the number of traded cryptocurrencies and the cryptocurrency market in general.In physics,the Schwarzschild radius indicates that black holes are constantly expanding because of their mass increase.Enriching this analogy,we consider the cryptocurrency market as a self-gravitational body whose mass is denoted by(1)the number of traded cryptocurrencies and(2)in terms of increasing market capitalization for a given number of traded cryptocurrencies.By analyzing weekly snapshot data of all traded cryptocurrencies from January 4,2009,to June 14,2020,we find evidence that the above-mentioned mechanism exists.The results clearly indicate the self-gravitational property of the cryptocurrency market,which is direct evidence toward the hypothesis that the changes in the traded cryptocurrencies are a positive function of the previous period’s number of traded cryptocurrencies.

A Two-phase Solution Algorithm for the Inventory Routing Problem with Time Windows

The main objective of this paper is to propose a two-phase solution algorithm for solving the Inventory Routing Problem with Time Windows （IRPTW）, which has not been excessively researched in the literature. The solution approach is based on （a） a simple simulation for the planning phase （Phase I） and （b） the Variable Neighborhood Search Algorithm （VNS） for the routing phase （Phase II）. Testing instances are established to investigate algorithmic performance, and the computational results are then reported. The computational study underscores the importance of integrating the inventory and vehicle routing decisions. Graphical presentation formats are provided to convey meaningful insights into the problem.

A privacy-preserving statistics marketplace using local differential privacy and blockchain: An application to smart-grid measurements sharing

Service providers usually require detailed statistics in order to improve their services.On the other hand,privacy concerns are intensifying and sensitive data is protected by legislation,such as GDPR(General Data Protection Regulation).In this paper,we present the design,implementation,and evaluation of a marketplace that allows“data consumers”to buy information from“data providers”,which can then be used for generating meaningful statistics.Additionally,our system enables“system operators”that can select which data providers are allowed to provide data,based on filtering criteria specified by the data consumer.We leverage local differential privacy to protect the data provider's privacy against data consumers,as well as against system operators,and we build a blockchain-based solution for ensuring fair exchange,and immutable data logs.Our design targets use cases that involve hundreds or even thousands of data providers.We prove the feasibility of our approach through a proof-of concept implementation of a measurement sharing application for smart-grid systems.

Geopolitical risks, uncertainty, and stock market performance

This paper analyses the impact of geopolitical risks and economic policy uncertainty on stock returns.It uses an unbalanced panel dataset of monthly observations for 22 countries for the period 1985–2020.It controls for a set of macroeconomic and market structure variables while also taking into consideration the poten-tial effects of the 2007–2009 financial crisis.This paper shows that the impact of geopolitical risks is negative and statistically signifi-cant.The economic interpretation of our main results is that a one-unit standard deviation increase in geopolitical risks decreases stock returns by 10.53–42.14%of its sample mean.For comparison,this paper uses alternatively the global economic policy uncertainty index and the economic policy uncertainty country index,and also finds a statistically significant negative relationship although it is weaker in the latter case.