Institutional Repository of Academic Research University of Macedonia
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SMART GLASS APPLICATION TESTING FOR AUGMENTED REALITY
No full textA multitude of mobile device applications are being designed every day, a significant amount of which are Augmented Reality applications. Current smartphones and tablets can use such applications to supersede multi-modal information, such as text, images or predesigned 3D digital objects, over the camera’s real-world input. This technology offers the users immersive educational, industrial, touristic and even research experiences. The main drawback of such applications is that the users have to hold their device towards their view point and look at the screen, which limits their mobility. Smart glasses are believed to be the next big thing in wearable technology, since they can offer an always-on augmented experience, without such constraints. The users are able to move and act in any way they wish, with the glasses working on certain cues, such as location beacons, image or object recognition and others. These devices are already seeing some use in industry, while pilot projects and experiments with them are also being performed in other fields. Smart glasses are expected to improve significantly and become more accessible to the public in the following few years, as technological advancements in computing and wearable technologies progress. Microsoft has already introduced the HoloLens, which is not yet a consumer device but is eventually aiming to be, while Apple is also working on a consumer headset that is expected to be complete and available in the next few years. In this paper we review the methodology used in testing mobile applications before publication, from the designer’s point of view, both for Android and iOS. We then use this current methodology and initially apply it to an Augmented Reality application for smartphones. Following that, we test it on the modified version of the same application for smart glasses. That includes testing both the hardware aspects, such as power efficiency testing, GPS accuracy testing etc, and the software aspects, for example usability, readability, high contrast options or colourblind mode etc. Our goal is to determine whether this set of guidelines can work for both cases and devices, since the original criteria suggested were only with handheld devices in mind and for touch focused applications, where the user has to look at his/her smartphone and all content is generated through it. We then seek to determine what, if any, extra criteria or new steps would be necessary for testing applications for Smart Glasses and offer our own set of criteria recommendations on the subject. Smart glass applications are, in our opinion, the next big thing in educational mobile applications, be it in virtual educational tours or 3D modelled reconstructed environments or even used as testbeds, for example in experimenting with cultural heritage artifacts without physical contact. Our testing and suggestions can benefit educators, stakeholders and designers by offering a better understanding of the challenges such applications can present, and change the way they are designed and tested for publication, in preparation for the wider use of Smart glasses.195699577EDULEARN22 Proceeding
A reinforcement learning—Variable neighborhood search method for the capacitated Vehicle Routing Problem
No full textFinding the best sequence of local search operators that yield the optimal performance of Variable Neighborhood Search is an important open research question in the field of metaheuristics. This paper proposes a Reinforcement Learning method to address this question. We introduce a new hyperheuristic scheme, termed Bandit VNS, inspired by the Multi-armed Bandit, a particular type of a single state reinforcement learning problem. In Bandit VNS, we utilize the General Variable Neighborhood Search metaheuristic and enhance it by a hyperheuristic strategy. We examine several variations of the Upper Confidence Bound algorithm to create a reliable strategy for adaptive neighborhood selection. Furthermore, we utilize Adaptive Windowing, a state of the art algorithm to estimate and detect changes in the data stream. Bandit VNS is designed for effective parallelization and encourages cooperation between agents to produce the best solution quality. We demonstrate this concept's advantages in accuracy and speed by extensive experimentation using the Capacitated Vehicle Routing Problem. We compare the novel scheme's performance against the conventional General Variable Neighborhood Search metaheuristic in terms of the CPU time and solution quality. The Bandit VNS method shows excellent results and reaches significantly higher performance metrics when applied to well-known benchmark instances. Our experiments show that, our approach achieves an improvement of more than 25% in solution quality when compared to the General Variable Neighborhood Search method using standard library instances of medium and large size.11881
Are candidate countries converging with the EU in terms of the Copenhagen political criteria?
No full textThe Copenhagen criteria for EU accession are the essential preconditions that candidate countries must satisfy to be deemed eligible for membership. In line with the strand of the literature that focuses on candidate countries’ convergence with the EU, the paper examines whether converge with the EU in terms of the Copenhagen political criteria can be established empirically for candidate and potential-candidate countries. Currently, the candidate country status is granted to Albania, North Macedonia, Montenegro, Serbia and Turkey. Bosnia-Herzegovina and Kosovo are recognised by the EU as potential candidate countries. In addition to the candidate and potential candidate countries, we include in the convergence tests the six countries of the Eastern Partnership EU policy: Armenia, Azerbaijan, Belarus, Georgia, Moldova, Ukraine. Using unit root tests, convergence is examined in terms of two indices drawn from the Varieties of Democracy (V-Dem) project: the Liberal democracy and the Civil liberties indices. The findings reported herein, are not uniform and on the whole offer only scant evidence in favour of the convergence hypothesis. For some of the candidate and potential candidate countries convergence is established, while for others the results do not point to such a process.23563965
Burnout and Cognitive Functioning: Are We Underestimating the Role of Visuospatial Functions?
No full textJob burnout is a psychological syndrome which results from chronic occupational stress and cognitive impairments are among its negative consequences. The demands of the COVID-19 pandemic have challenged the healthcare system increasing the risk of job burnout among healthcare professionals. The studies conducted so far have mainly focused on the effects of job burnout on executive functions. Visuospatial functions are a cognitive domain which plays an important role in healthcare workers' optimal performance. Healthcare workers are constantly relying on their visuospatial abilities in order to care for their patients as they are required to use techniques that involve manipulation of medical instruments, they need to have excellent hand-eye coordination and great perception of spatial anatomy, factors that can affect healthcare workers' performance is of significance and can put patient safety at risk. However, our understanding of how visuospatial functions are being affected in job burnout is limited. The scope of this mini-review is to examine the evidence concerning the relationship of job burnout with visuospatial functions. The sparsity of the relevant empirical evidence does not allow for definite conclusions. However, given the implications of diminished visuospatial abilities in patient safety we highlight the need for studies exploring the effects of job burnout on visuospatial functions. Limitations of studies are discussed.1
Citation based journal-to-journal associations in the microcosm of an academic libraries consortium
No full textScholarly journals are usually rated on the basis of the number of citations received which directly translates to impact in the advancement of research. In this paper, the emphasis is on considering citations as a means for unveiling journal-to-journal associations. An Academic Libraries Consortium could use such kind of information in the process of planning for the collection of journal titles (subscriptions) that best serves the needs of its research user community. The innovative contribution is summarized as follows: (a) journal research impact measures are considered in the context of the well-defined user community (Target Authors Group, TAG) of the Hellenic Academic Libraries Link (HEAL-Link) Consortium, and (b) research impact measures are used to reveal interesting journal-to-journal associations, as well as journals of an interdisciplinary value, specifically for the microcosm of the given TAG community. The research outcomes are publicized by means of a number of interactive graphs via the J2J-GR web service (https://j2j.heal-link.gr). The latter is expected to be of use not just for the HEAL-Link office staff, but also for all the interested researchers, in Greece or abroad.48110246
Consolidating incentivization in distributed neural network training via decentralized autonomous organization
No full textBig data has reignited research interest in machine learning. Massive quantities of data are being generated regularly as a consequence of the development in the Internet, social networks, and online sensors. Particularly deep neural networks benefited greatly from this unprecedented data availability. Large models with millions of parameters are becoming common, and big data has been proved to be essential for their effective training. The scientific community has come up with a number of methods to create more accurate models, but most of these methods require high-performance infrastructure. There is also the issue of privacy, since anyone using leased processing power from a remote data center is putting their data in the hands of a third party. Studies on decentralized and non-binding methods among individuals with commodity hardware are scarce, though. Our work on LEARNAE seeks to respond to this challenge by creating a totally distributed and fault-tolerant framework of artificial neural network training. In our recent work, we demonstrated a method for incentivizing peers to participate to collaborative process, even if they are not interested in the neural network produced. For this, LEARNAE included a subsystem that rewards participants proportionately to their contribution using digital assets. In this article we add another important piece to the puzzle: A decentralized mechanism to mitigate the effect of bad actors, such as nodes that attempt to exploit LEARNAE’s network power without following the established rewarding rules. This is achieved by a novel reward mechanism, which takes into account the overall contribution of each node to the entire swarm. The network collaboratively builds a contribution profile for every participant, and the final rewards are dictated by these profiles. Taking for granted that the majority of the peers are benevolent, the whole process is tamper-proof, since it is implemented on blockchain and thus is protected by distributed consensus. All codebase is structured as a decentralized autonomous organization, which allows LEARNAE to embed new features like digital asset locking, proposal submitting, and voting
Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece
No full textBoth Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing.13419
Euro area stock markets integration: Empirical evidence after the end of 2010 debt crisis
No full textThe Euro Area faces plenty of financial and economic asymmetries on account of the dissimilar economies’ participation in the union. The long-term financial integration of the EA member-states constitutes a significant task for the EU policy makers in business and economic terms. This letter investigates the degree of stock markets integration in the Eurozone after the end of 2010 debt-crisis. The results reveal that the stock market integration be strong between Germany and EA core member-states but disparate for the EA periphery. In contrast, there are only indications regarding the EA Eastern Mediterranean and Baltic stock markets integration with DAX-30.4610242
Financial development, reforms and growth
No full textIs there any specific structure of the financial system which promotes economic growth or does this structure depend on the level of economic growth itself? Financial development and financial reforms affect economic growth, but less is known on how this effect varies across different levels of the conditional distribution of the growth rates. We examine this by using panel data for 81 countries for more than 30 years. We account for unobserved heterogeneity and operate within alternative econometric approaches. The findings indicate that financial reforms are important determinants of growth, especially when a country faces relatively low levels of economic growth. Financial development does matter for growth, however, the size and significance of the effect vary. Financial reforms affect economic growth more than financial development. We reveal that the components of financial reforms, which are more important for economic growth, are the supervision of banks and the regulation of securities markets.10810573
Forecasting and explaining emergency department visits in a public hospital
No full textEmergency Departments (EDs) are the most overcrowded places in public hospitals. Machine learning can support decisions on effective ED resource management by accurately forecasting the number of ED visits. In addition, Explainable Artificial Intelligence (XAI) techniques can help explain decisions from forecasting models and address challenges like lack of trust in machine learning results. The objective of this paper is to use machine learning and XAI to forecast and explain the ED visits on the next on duty day. Towards this end, a case study is presented that uses the XGBoost algorithm to create a model that forecasts the number of patient visits to the ED of the University Hospital of Ioannina in Greece, based on historical data from patient visits, time-based data, dates of holidays and special events, and weather data. The SHapley Additive exPlanations (SHAP) framework is used to explain the model. The evaluation of the forecasting model resulted in an MAE value of 18.37, revealing a more accurate model than the baseline, with an MAE of 29.38. The number of patient visits is mostly affected by the day of the week of the on duty day, the mean number of visits in the previous four on duty days, and the maximum daily temperature. The results of this work can help policy makers in healthcare make more accurate and transparent decisions that increase the trust of people affected by them (e.g., medical staff).59247950