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Experience with Managing Technical Debt in Scientific Software Development using the EXA2PRO framework
No full textTechnical Debt (TD) is a software engineering metaphor that resembles the production of poor-quality code to going into debt. In particular, a development team that "saves" effort while developing by not removing inefficiencies, has to "pay-back" with interest, in the form of additional maintenance costs (i.e., fixing bugs, adding features, etc.) due to the poor maintainability of the developed code. Although maintainability assurance is an established practice in traditional software development (lately known as TD management), it has still not attracted the attention of scientific software developers; i.e., researchers writing code and developing tools for purely research purposes. Nevertheless, based on the literature and practice, maintainability seems to be ranked as an important key-driver for the development of such applications; since the effort needed to update the code before the experimentation (e.g., executing a simulation) is common and should not receive low priority. In this paper, we present the outcome of a 3-year research project on Technical Debt Management (TDM) for scientific software development. The outcome of the project is a framework (termed: EXA2PRO TDM framework) and an accompanying platform for assisting scientific software developers in managing the TD of their applications. The framework is a collection of methods tailored for the mainstream programming languages of scientific software development, which have been empirically validated through five pilot applications. The majority of the EXA2PRO framework suggestions have been applied by scientific software developers and eased future maintenance activities.11
Incentivizing Participation to Distributed Neural Network Training
No full textDuring the last years a vast number of online sensors continuously generate data that can be utilized to create novel deep learning applications. Training very large models requires enormous processing power; thus, the evident way to follow is to lease the power of a corporate data center. But the diffusion of Artificial Intelligence to an always increasing number of human activities, constantly attracts new researchers who wish to train and test their models. Our work on LEARNAE is a proposal for a purely distributed neural network training, based on a peer-to-peer and permissionless architecture. LEARNAE allows individual researchers to join forces, in order to collaboratively train a model. The process utilizes modern Distributed Ledger Technology and it is fully democratized, prioritizing decentralization, fault tolerance and privacy. In this paper we add another piece to the puzzle: A method for incentivizing peers to participate to the training swarm, even if they don’t have any interest in the produced neural network. This is achieved by embedding a reward subsystem to LEARNAE; thus, peers who contribute to teamwork can receive a proportional digital payment.364374Proceedings of the 22nd Engineering Applications of Neural Networks Conferenc
Job satisfaction behind motivation: An empirical study in public health workers
No full textThe health sector is characterized as labor-intensive, which means that the effectiveness of an organization that operates within its context is inextricably linked to the level of employee performance. Therefore, an essential condition, in order to achieve higher standards, in terms of the effectiveness of the health units, as well as set the foundations of a solid health system, is to take maximum advantage of the full potential of human resources. This goal can only be accomplished by providing the appropriate incentives, which will naturally cause the adoption of the desired attitude and behavior. In the case of Greece, there is not enough research relative to the needs of health workers and, consequently, the incentives that can motivate them. This article aims to investigate the dynamics that may be behind health workers at a public hospital in Northern Greece. Data were collected from 74 employees in the hospital and were analyzed using ANOVA analysis. The results show that key motivators for the employees can be considered the relationships with their colleagues and the level of achievement, while the level of rewards and job characteristics play a secondary role. These results make it clear that, in order for the hospital's management to be able to improve the level of employee performance, it should ensure the establishment of a strong climate among employees, and also acknowledge the efforts made by them.74e0685
Remote biometric identification systems and ethical challenges: The case of facial recognition
No full textArtificial Intelligence (AI) is the field of computer science progressing rapidly, simulating human behavior and thinking and affecting already the most of economic and social activities. The use of AI has undoubtedly positive outcomes but also implies high-risks. One of the most common AI applications is the case of facial recognition, a remote biometric identification system that raises the conversation around the intrusion into individuals’ privacy. In an attempt to balance ethical values and legal principles concerning, inter alia, automated recognition, European Commission puts forward a regulatory framework with specific objectives. The paper aims to highlight the precautionary principle that EU enforces in the case of facial recognition practice, establishing security mechanisms with the aim of mining, re-using and sharing data that are required for the buildout of a reliable Artificial Intelligence.162021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM
The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review
No full textEarly and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.1023298
The Impact of Economic Complexity on the Formation of Environmental Culture
No full textThis paper establishes economic complexity as a powerful predictor of environmental attitudes. While the economic complexity index (ECI) has been associated with a series of economic outcomes, yet there has not been a link in the literature between ECI and environmental attitudes. This research pushes forward the hypothesis that economic complexity shapes cultural values and beliefs. The research method used is a multilevel empirical analysis that associates aggregate values of the ECI, at the country level, with individual responses related to attitudes towards the environment. Our findings suggest that a marginal increase of the ECI, increases by 0.191 the probability to be a member of environmental organisations and an increase by 0.259 in the probability to engage in voluntary work for the environment. To further reinforce our findings by ensuring identification we replicate the benchmark analysis using as a proxy of a country’s level of economic complexity, the average ECI of the neighbouring countries (weighted by population and/or volume of trade). With a similar intention, i.e., to mitigate endogeneity concerns as well as to further frame our findings as "the cultural implications of ECI" we replicate our analysis with a sample of second generation immigrants. The immigrant analysis, suggests that the level of economic complexity of the parents’ country of origin, has a long-lasting effect on second generation immigrants’ attitudes related to the environment. Because humankind’s attitudes and actions are of key importance for a sustainable future, a better understanding as to what drives environmental attitudes appears critical both for researchers and policy makers.13287
The temporality of technical debt introduction on new code and confounding factors
No full textCode Technical Debt (TD) is intentionally or unintentionally created when developers introduce inefficiencies in the codebase. This can be attributed to various reasons such as heavy workload, tight delivery schedule, or developers’ lack of experience. Since a software system grows mostly through the addition of new code, it is interesting to study how TD fluctuates along this process. Specifically, in this paper, we investigate: (a) the temporality of code TD introduction in new code, i.e., whether the introduction of TD is stable across the lifespan of the project, or if its evolution presents spikes; and (b) the relation of TD introduction to the development team’s workload in a given period, as well as to the experience of the development team. To answer these questions, we have performed a case study on 47 open-source projects from two well-known ecosystems (Apache and Eclipse) as well as additional isolated projects from GitHub (not selected from a specific ecosystem) and inspected the number of TD issues introduced in 6-month sliding temporal windows. The results of the study suggested that: (a) overall, the number of TD issues introduced through new code is a stable measure, although it presents spikes; and (b) the number of commits performed, as well as developers’ experience are not strongly correlated to the number of introduced TD issues
Towards Co-creating Getting a Transport Card Integrated Public Service
No full textToday, citizens demand personalised and integrated electronic public services that match their exact needs and circumstances regardless of the number of actual public authorities involved. This can be realised by combining research and practice in the fields of integrated public service and service co-creation. The aim of this paper is to understand stakeholders’ views towards providing a co-created, integrated public service in a Greek Region. More specifically, the public service Getting a Transport Card was analysed. This public service is available to low-income, disabled citizens providing them with free or reduced-price transportation. To meet our aim, we conducted structured interviews with citizens, region employees, and policy makers. The results provided us a clearer understanding of stakeholders views regarding system requirements, the role of co-creation, integrated public service governance, interoperability layers (legal, organisational, semantic and technical), and sustainability. These enabled us to formulate a clear usage scenario for a new integrated public service.51351614th International Conference on Theory and Practice of Electronic Governanc
A graph neural network method for distributed anomaly detection in IoT
No full textRecent IoT proliferation has undeniably affected the way organizational activities and business procedures take place within several IoT domains such as smart manufacturing, food supply chain, intelligent transportation systems, medical care infrastructures etc. The number of the interconnected edge devices has dramatically increased, creating a huge volume of transferred data susceptible to leakage, modification or disruption, ultimately affecting the security level, robustness and QoS of the attacked IoT ecosystem. In an attempt to prevent or mitigate network abnormalities while accommodating the cohesiveness among the involved entities, modeling their interrelations and incorporating their structural, content and temporal attributes, graph-based anomaly detection solutions have been repeatedly adopted. In this article we propose, a multi-agent system, with each agent implementing a Graph Neural Network, in order to exploit the collaborative and cooperative nature of intelligent agents for anomaly detection. To this end, against the propagating nature of cyber-attacks such as the Distributed Denial-of-Service (DDoS), we propose a distributed detection scheme, which aims to monitor efficiently the entire network infrastructure. To fulfill this task, we consider employing monitors on active network nodes such as IoT devices, SDN forwarders, Fog Nodes, achieving localization of anomaly detection, distribution of allocated resources such as the bandwidth and power consumption and higher accuracy results. In order to facilitate the training, testing and evaluation activities of the Graph Neural Network algorithm, we create simulated datasets of network flows of various normal and abnormal distributions, out of which we extract essential structural and content features to be passed to neighbouring agents.12193