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Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding
No full textNetwork Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. In this respect, there is an increasing need for intelligent orchestration mechanisms, such that NFV can exploit its potential and live up to its promise. Genetic algorithms have emerged as a promising alternative to the proliferation of heuristic and exact methods for the Service Function Chain (SFC) embedding problem. To this end, we design and evaluate a genetic algorithm (GA), which computes efficient embeddings with runtimes on par with approximate methods. We present a GA model as state-space search in order to clarify the design choices of a GA. Our proposed GA utilizes a heuristic for the generation of the initial population, with the aim of directing the search towards the solution. Given the sensitivity of GAs on their various parameters, we introduce a parameter adjustment framework for GA fine-tuning. A comparative evaluation among a range of GA variants with diverse features sheds light on the impact of these features on SFC embedding efficiency. The GA variant that stands out is further benchmarked against a baseline greedy algorithm and a state-of-the-art heuristic. Our evaluation results indicate that the GA yields notable gains in terms of request acceptance and resource efficiency.31
Protocol-Adaptive Strategies for Wireless Mesh Smart City Networks
No full textWireless mesh networks, especially those typically utilized in smart city deployments for their low-cost and adaptable topologies, are characterized by challenging requirements for communication performance, reliability, as well as adaptability to dynamic network conditions. In this context, Named Data Networking (NDN) introduces a novel packet naming scheme and in-network caching for efficient data retrieval. Although NDN reliability can be damaged by prolonged delays and intermittent connectivity, this impact can be largely canceled by incorporating the Delay-Tolerant-Networking (DTN) paradigm. Hence, we argue that the challenging, dynamic network conditions of smart cities can be handled accordingly by a protocol-adaptive solution that deploys and configures on-demand the most appropriate protocol strategy per node. Software-Defined Networking (SDN) provides the missing features of intelligent centralized control and programmability. Here, we propose REWIRE: an SDN-based protocol-adaptive solution for smart city networking with low-delay communication and reliable interactions. We employ SDN control features, containerized non-IP protocol stacks, clustering and change point (CCP) mechanisms. We also conduct a preliminary investigation of our solution based on real experimentation over two novel smart city testbeds.37213614
Smart contract applications in tourism
No full textBased on blockchain technology, smart contracts promise to revolutionize the way parties legally agree. Smart contracts could enable tourism service providers to trade directly with customers bypassing some intermediaries. The study aims at identifying the provided services, economic impact, partners, popularity, technical and technological factors of smart contract applications in various tourism areas. It investigates ten popular smart contract applications that cover a wide spectrum of tourism areas such as hotel reservations, airline tickets, car rentals, payment management, reward programmes, traveller identity, luggage tracking, validity of reviews and ratings and more. These applications are analysed with respect to their purpose, business model, economic impact, partners, provided services, popularity, as well as what cryptocurrency and blockchain they use. Smart contracts enable time and transaction cost savings, convenience, flexibility, security, trust, ease verification of personal data and more. Most applications gained popularity mainly during 2018–19. Almost every application uses a different cryptocurrency. Ethereum is the most popular platform followed by Hyperledger Fabric and Stellar. The development of a universal legislation as well as interoperability is a necessity for the wide adoption of smart contracts.22216518
Very fast variations of training set size reduction algorithms for instance-based classification
No full textReduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) are effective data reduction techniques for the k-NN classifier. They are based on an iterative k-means clustering task that discovers homogeneous clusters. The centers of the resulting homogeneous clusters constitute the instances of the reduced training set. Although RHC and ERHC are quite fast compared to several well-known data reduction techniques, the iterative execution of k-means clustering renders both of them inappropriate for data reduction tasks that need to be performed quickly, especially, when run over large training datasets. The present paper proposes simple and very fast variations of the algorithms, which are appropriate for such environments. The variations are called RHC2 and ERHC2 and replace the complete execution of k-means clustering with a fast task that assigns instances to the class centers. The experimental study based on fourteen datasets, and, the corresponding statistical tests, show that the proposed RHC2 and ERHC2 variations are very fast and, at the cost of a small penalty on classification accuracy, they achieve higher reduction rates than their predecessors and other two well-known data reduction techniques. They are good candidates when fast reduction on large datasets is required.6470International Database Engineered Applications Symposium Conferenc
Conceptualizing and validating a customer satisfaction measurement model in e-services. Evidence from the Greek e-banking sector
No full textThis study aimed to measure customer satisfaction by employing the five key dimensions of the SERVQUAL model. The dimensions of the SERVQUAL model and a focus group method was used to conceptualise a research model proposed for customer satisfaction measurement in the Greek e-banking sector. A questionnaire was designed and administered to 1,026 e-banking users, and a total of 333 respondents replied. For assessment of the measurement model, exploratory and confirmatory factor analysis was used. Additionally, for assessment of the structural model, the partial least squares (PLS) structural equation modelling (SEM) technique was employed. The validated customer satisfaction measurement tool includes three dimensions, namely performability, responsiveness, and assurance, which have significant and positive relationships with the customer satisfaction construct. Due to the locality of the context, we suggest that further research should be conducted to generalise the findings in e-banking and e-commerce. This paper is the first attempt to provide a validated customer-satisfaction-measurement model based on the dimensions of the SERVQUAL model that directly asks customers to weight service attributes while using a tool based on statistical inference from observed associations.1
Investigating the Serially Mediating Mechanisms of Organizational Ambidexterity and the Circular Economy in the Relationship between Ambidextrous Leadership and Sustainability Performance
No full textIn this study, based on a resource-based view, we investigate the influence of ambidextrous leadership (reflected in transformational and transactional leadership styles) on sustainability performance (reflected in economic, environmental, and social performance) through the serially mediating mechanisms of organizational ambidexterity (reflected in explorer and exploiter attributes) and the circular economy (reflected in fields of action). By applying structural equation modelling analyses to survey data collected from private and public Greek organizations, which operate in manufacturing, services, and trade sectors, under an externally dynamic environmental context, we found that (a) organizational ambidexterity and the circular economy fields of action positively mediate the relationship between ambidextrous leadership and sustainability performance and (b) the mechanism originating from transformational leadership has a higher impact on sustainability performance compared to the mechanism that originates from transactional leadership. Accordingly, this study addresses the aspect of the special issue that refers to modern approaches to management and leadership for sustainable business performance research and makes several theoretical and practical implications.1510793
Transforming computed tomography scans into a full-immersive virtual museum for the Antikythera Mechanism
No full textThe extended research of the Computed Tomography (CT) scans of the fragments of the Antikythera Mechanism was the cornerstone for revealing the structure and functions of the first mechanical computer. In this article we present a unique methodology for the transformation of CT-scans files into photorealistic three-dimensional (3D) objects where other techniques, such as photogrammetry or 3D scanning, cannot be applied due to specific obstacles. These digital assets were reconstructed in the Lab, by using the CT-scans dataset for mesh generation, and only two high-resolution images for applying the textured materials. A quantitative evaluation from 62 volunteers’ testers of the Virtual Museum that hosts the digital representations of the fragments of the Antikythera Mechanism, confirms the high level of the achieved photorealism, in addition to usability, usefulness, and level of satisfaction.28e0025
Bribery, on-the-job training, and firm performance
No full textThe previous literature has extensively examined the effect of firm-level bribery on firm performance but not through on-the-job training. This paper investigates the impact of paying bribes on the firm’s investment decisions in on-the-job training and offers mediating implications of corruption on firm performance. We empirically examine the relationship between bribery and on-the-job training using firm-level data from the World Bank Enterprise Surveys consisting of a sample of 94 developing countries with 20,601 firms. The findings show that bribery and on-the-job training intensity affects real annual sales growth rates negatively and positively, respectively. Furthermore, firms exposed to more bribery reduce their on-the-job training intensity. The results are robust to the different classifications of the firm’s size, different subsamples, and controls for the endogeneity of the on-the-job training and bribery.601375
The Future of AI in Ovarian Cancer Research: The Large Language Models Perspective
No full textConversational large language model (LLM)-based chatbots utilize neural networks to process natural language. By generating highly sophisticated outputs from contextual input text, they revolutionize the access to further learning, leading to the development of new skills and personalized interactions. Although they are not developed to provide healthcare, their potential to address biomedical issues is rather unexplored. Healthcare digitalization and documentation of electronic health records is now developing into a standard practice. Developing tools to facilitate clinical review of unstructured data such as LLMs can derive clinical meaningful insights for ovarian cancer, a heterogeneous but devastating disease. Compared to standard approaches, they can host capacity to condense results and optimize analysis time. To help accelerate research in biomedical language processing and improve the validity of scientific writing, task-specific and domain-specific language models may be required. In turn, we propose a bespoke, proprietary ovarian cancer-specific natural language using solely in-domain text, whereas transfer learning drifts away from the pretrained language models to fine-tune task-specific models for all possible downstream applications. This venture will be fueled by the abundance of unstructured text information in the electronic health records resulting in ovarian cancer research ultimately reaching its linguistic home.3
Unsupervised Deep Learning for Distributed Service Function Chain Embedding
No full textNetwork Function Virtualization (NFV) has paved the way for the migration of Virtual Network Functions (VNFs) into multi-tenant datacenters, lowering the barrier for the introduction of new processing functionality into the network. Recent trends for resource orchestration across the entire compute continuum raise the need for decision making at low timescales, a requirement which can be hardly met by centralized resource optimizers that rely either on Linear Programming or Machine Learning (ML). In this respect, we present a distributed approach tailored to a crucial resource orchestration aspect, i.e., the embedding of Service Function Chains (SFCs) onto large-scale virtualized network infrastructures. In order to confront the computational hardness of the SFC embedding problem, we utilize a clustering method for the partitioning of the solution space, empowering the search for efficient solutions in parallel across all clusters. Another salient feature of our approach is the use of unsupervised deep learning for the computation of embeddings within each cluster. Our distributed SFC embedding framework is benchmarked against a state-of-the-art heuristic and a distributed greedy algorithm. Our evaluation results uncover notable gains in terms of resource efficiency, combined with solver runtimes in the order of milliseconds with thousands of substrate nodes.11916609167