Academic literature on the topic 'Semantic fog computing'

Cloud computing is equipped with the numerous of advantageous features to support software and utilities on the Internet of Things (IoT). Cloud-based technology is widely used when offering support for heterogeneous applications integrating specific IoT that follows various semantics. Attaching additional information to raw data sensed with the help of ontology is accomplished in semantic model. The longer distance between the cloud and IoT applications, however, is a bottleneck for vital IoT software. So the paper puts forth a semantic frame work assisted by the fog to enhance the interoperability in the internet of things. The structure put forth moves some of the cloud's commonly used semantic resources sensor networks edge and also offers an effective off-loading technique between fog – fog and fog – cloud devices to diminish total computation time of the task and the energy consumed by the nodes in the fog. The proposed method further follows an efficient mapping technique to transform the data’s sensed into a RDF-format such that it is compatible for processing. The proposed model is evaluated on the basis of delay in the service provision, the energy consumed , and the total cost of the system and further the results obtained are compared with the relevant cloud based computing models , to reveal the proficiency of the proposed.

Purpose The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dynamically change their geographic location. In order to exploit the true potential of cloud continuum computing applications, it is essential to use a comprehensive set of various intricate technologies together. This complex blend of technologies currently raises data interoperability problems in such modern computing frameworks. Therefore, a semantic model is required to unambiguously specify notions of various concepts employed in cloud applications. The goal of the present paper is therefore twofold: (i) offering a new model, which allows an easier understanding of microservices within adaptive fog computing frameworks, and (ii) presenting the latest open standards and tools which are now widely used to implement each class defined in our proposed model.

The emergence of fog and edge computing has complemented cloud computing in the design of pervasive, computing-intensive applications. The proximity of fog resources to data sources has contributed to minimizing network operating expenditure and has permitted latency-aware processing. Furthermore, novel approaches such as serverless computing change the structure of applications and challenge the monopoly of traditional Virtual Machine (VM)-based applications. However, the efforts directed to the modeling of cloud applications have not yet evolved to exploit these breakthroughs and handle the whole application lifecycle efficiently. In this work, we present a set of Topology and Orchestration Specification for Cloud Applications (TOSCA) extensions to model applications relying on any combination of the aforementioned technologies. Our approach features a design-time “type-level” flavor and a run time “instance-level” flavor. The introduction of semantic enhancements and the use of two TOSCA flavors enables the optimization of a candidate topology before its deployment. The optimization modeling is achieved using a set of constraints, requirements, and criteria independent from the underlying hosting infrastructure (i.e., clouds, multi-clouds, edge devices). Furthermore, we discuss the advantages of such an approach in comparison to other notable cloud application deployment approaches and provide directions for future research.

Through the Internet of Things (IoT), Smart Spaces will enable environments to adapt according to users’ needs by using smart and connected objects. However, to turn the IoT view into a reality, the users should know about technical details of such objects, which is not a trivial task for most ordinary users. Therefore, this paper presents FoT-Rules, an approach for the construction of semantic rules aiming to create Smart Spaces through Fog of Things, which is a paradigm for Fog Computing in the IoT. FoT-Rules is designed to enable ordinary users to create and execute semantic rules in the Event-Condition-Action standard (ECA) and to take actions at the edge of the network. In this work, we present a scenario where the user can create semantic rules in the ECA standard and, in order to execute these rules at the network edge, FoT-Rules provides the following functionalities: creation of semantic rules; obtaining of the semantic models that contains information related to IoT devices; execution of a semantic reasoner over the semantic model according to the rule created by the user; a semantic observer that is responsible for observing changes in IoT devices; and in case the rule created by the user is activated, an action is taken for an IoT device. Finally, we performed four types of evaluations on our FoT-Rules approach: reliability, efficiency, scalability and usability.

Recent Trends and Future of Fog and Edge Computing, Services, and Enabling Technologies
 Cloud computing has been established as the most popular as well as suitable computing infrastructure providing on-demand, scalable and pay-as-you-go computing resources and services for the state-of-the-art ICT applications which generate a massive amount of data. Though Cloud is certainly the most fitting solution for most of the applications with respect to processing capability and storage, it may not be so for the real-time applications. The main problem with Cloud is the latency as the Cloud data centres typically are very far from the data sources as well as the data consumers. This latency is ok with the application domains such as enterprise or web applications, but not for the modern Internet of Things (IoT)-based pervasive and ubiquitous application domains such as autonomous vehicle, smart and pervasive healthcare, real-time traffic monitoring, unmanned aerial vehicles, smart building, smart city, smart manufacturing, cognitive IoT, and so on. The prerequisite for these types of application is that the latency between the data generation and consumption should be minimal. For that, the generated data need to be processed locally, instead of sending to the Cloud. This approach is known as Edge computing where the data processing is done at the network edge in the edge devices such as set-top boxes, access points, routers, switches, base stations etc. which are typically located at the edge of the network. These devices are increasingly being incorporated with significant computing and storage capacity to cater to the need for local Big Data processing. The enabling of Edge computing can be attributed to the Emerging network technologies, such as 4G and cognitive radios, high-speed wireless networks, and energy-efficient sophisticated sensors.
 Different Edge computing architectures are proposed (e.g., Fog computing, mobile edge computing (MEC), cloudlets, etc.). All of these enable the IoT and sensor data to be processed closer to the data sources. But, among them, Fog computing, a Cisco initiative, has attracted the most attention of people from both academia and corporate and has been emerged as a new computing-infrastructural paradigm in recent years. Though Fog computing has been proposed as a different computing architecture than Cloud, it is not meant to replace the Cloud. Rather, Fog computing extends the Cloud services to network edges for providing computation, networking, and storage services between end devices and data centres. Ideally, Fog nodes (edge devices) are supposed to pre-process the data, serve the need of the associated applications preliminarily, and forward the data to the Cloud if the data are needed to be stored and analysed further.
 Fog computing enhances the benefits from smart devices operational not only in network perimeter but also under cloud servers. Fog-enabled services can be deployed anywhere in the network, and with these services provisioning and management, huge potential can be visualized to enhance intelligence within computing networks to realize context-awareness, high response time, and network traffic offloading. Several possibilities of Fog computing are already established. For example, sustainable smart cities, smart grid, smart logistics, environment monitoring, video surveillance, etc.
 To design and implementation of Fog computing systems, various challenges concerning system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. are needed to be addressed. Also, to make Fog compatible with Cloud several factors such as Fog and Cloud system integration, service collaboration between Fog and Cloud, workload balance between Fog and Cloud, and so on need to be taken care of.
 It is our great privilege to present before you Volume 20, Issue 2 of the Scalable Computing: Practice and Experience. We had received 20 Research Papers and out of which 14 Papers are selected for Publication. The aim of this special issue is to highlight Recent Trends and Future of Fog and Edge Computing, Services and Enabling technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to Fog Computing, Cloud Computing and Edge Computing.
 Sujata Dash et al. contributed a paper titled “Edge and Fog Computing in Healthcare- A Review” in which an in-depth review of fog and mist computing in the area of health care informatics is analysed, classified and discussed. The review presented in this paper is primarily focussed on three main aspects: The requirements of IoT based healthcare model and the description of services provided by fog computing to address then. The architecture of an IoT based health care system embedding fog computing layer and implementation of fog computing layer services along with performance and advantages. In addition to this, the researchers have highlighted the trade-off when allocating computational task to the level of network and also elaborated various challenges and security issues of fog and edge computing related to healthcare applications.
 Parminder Singh et al. in the paper titled “Triangulation Resource Provisioning for Web Applications in Cloud Computing: A Profit-Aware” proposed a novel triangulation resource provisioning (TRP) technique with a profit-aware surplus VM selection policy to ensure fair resource utilization in hourly billing cycle while giving the quality of service to end-users. The proposed technique use time series workload forecasting, CPU utilization and response time in the analysis phase. The proposed technique is tested using CloudSim simulator and R language is used to implement prediction model on ClarkNet weblog. The proposed approach is compared with two baseline approaches i.e. Cost-aware (LRM) and (ARMA). The response time, CPU utilization and predicted request are applied in the analysis and planning phase for scaling decisions. The profit-aware surplus VM selection policy used in the execution phase for select the appropriate VM for scale-down. The result shows that the proposed model for web applications provides fair utilization of resources with minimum cost, thus provides maximum profit to application provider and QoE to the end users.
 
 Akshi kumar and Abhilasha Sharma in the paper titled “Ontology driven Social Big Data Analytics for Fog enabled Sentic-Social Governance” utilized a semantic knowledge model for investigating public opinion towards adaption of fog enabled services for governance and comprehending the significance of two s-components (sentic and social) in aforesaid structure that specifically visualize fog enabled Sentic-Social Governance. The results using conventional TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction are empirically compared with ontology driven TF-IDF feature extraction to find the best opinion mining model with optimal accuracy. The results concluded that implementation of ontology driven opinion mining for feature extraction in polarity classification outperforms the traditional TF-IDF method validated over baseline supervised learning algorithms with an average of 7.3% improvement in accuracy and approximately 38% reduction in features has been reported.
 
 
 Avinash Kaur and Pooja Gupta in the paper titled “Hybrid Balanced Task Clustering Algorithm for Scientific workflows in Cloud Computing” proposed novel hybrid balanced task clustering algorithm using the parameter of impact factor of workflows along with the structure of workflow and using this technique, tasks can be considered for clustering either vertically or horizontally based on value of impact factor. The testing of the algorithm proposed is done on Workflowsim- an extension of CloudSim and DAG model of workflow was executed. The Algorithm was tested on variables- Execution time of workflow and Performance Gain and compared with four clustering methods: Horizontal Runtime Balancing (HRB), Horizontal Clustering (HC), Horizontal Distance Balancing (HDB) and Horizontal Impact Factor Balancing (HIFB) and results stated that proposed algorithm is almost 5-10% better in makespan time of workflow depending on the workflow used.
 Pijush Kanti Dutta Pramanik et al. in the paper titled “Green and Sustainable High-Performance Computing with Smartphone Crowd Computing: Benefits, Enablers and Challenges” presented a comprehensive statistical survey of the various commercial CPUs, GPUs, SoCs for smartphones confirming the capability of the SCC as an alternative to HPC. An exhaustive survey is presented on the present and optimistic future of the continuous improvement and research on different aspects of smartphone battery and other alternative power sources which will allow users to use their smartphones for SCC without worrying about the battery running out.
 Dhanapal and P. Nithyanandam in the paper titled “The Slow HTTP Distributed Denial of Service (DDOS) Attack Detection in Cloud” proposed a novel method to detect slow HTTP DDoS attacks in cloud to overcome the issue of consuming all available server resources and making it unavailable to the real users. The proposed method is implemented using OpenStack cloud platform with slowHTTPTest tool. The results stated that proposed technique detects the attack in efficient manner.
 Mandeep Kaur and Rajni Mohana in the paper titled “Static Load Balancing Technique for Geographically partitioned Public Cloud” proposed a novel approach focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. Results are compared with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms. In this work, the researchers compared the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold.
 Mukta and Neeraj Gupta in the paper titled “Analytical Available Bandwidth Estimation in Wireless Ad-Hoc Networks considering Mobility in 3-Dimensional Space” proposes an analytical approach named Analytical Available Bandwidth Estimation Including Mobility (AABWM) to estimate ABW on a link. The major contributions of the proposed work are: i) it uses mathematical models based on renewal theory to calculate the collision probability of data packets which makes the process simple and accurate, ii) consideration of mobility under 3-D space to predict the link failure and provides an accurate admission control. To test the proposed technique, the researcher used NS-2 simulator to compare the proposed technique i.e. AABWM with AODV, ABE, IAB and IBEM on throughput, Packet loss ratio and Data delivery. Results stated that AABWM performs better as compared to other approaches.
 R.Sridharan and S. Domnic in the paper titled “Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment” proposed a novel heuristic IcAPER,(Inter-communication Aware Placement for Elastic Requests) algorithm. The proposed algorithm uses the network neighborhood machine for placement, once current resource is fully utilized by the application. The performance IcAPER algorithm is compared with First Come First Serve (FCFS), Random and First Fit Decreasing (FFD) algorithms for the parameters (a) resource utilization (b) resource fragmentation and (c) Number of requests having intercommunicating tasks placed on to same PM using CloudSim simulator. Simulation results shows IcAPER maps 34% more tasks on to the same PM and also increase the resource utilization by 13% while decreasing the resource fragmentation by 37.8% when compared to other algorithms.
 
 Velliangiri S. et al. in the paper titled “Trust factor based key distribution protocol in Hybrid Cloud Environment” proposed a novel security protocol comprising of two stages: first stage, Group Creation using the trust factor and develop key distribution security protocol. It performs the communication process among the virtual machine communication nodes. Creating several groups based on the cluster and trust factors methods. The second stage, the ECC (Elliptic Curve Cryptography) based distribution security protocol is developed. The performance of the Trust Factor Based Key Distribution protocol is compared with the existing ECC and Diffie Hellman key exchange technique. The results state that the proposed security protocol has more secure communication and better resource utilization than the ECC and Diffie Hellman key exchange technique in the Hybrid cloud.
 
 Vivek kumar prasad et al. in the paper titled “Influence of Monitoring: Fog and Edge Computing” discussed various techniques involved for monitoring for edge and fog computing and its advantages in addition to a case study based on Healthcare monitoring system.
 
 Avinash Kaur et al. elaborated a comprehensive view of existing data placement schemes proposed in literature for cloud computing. Further, it classified data placement schemes based on their assess capabilities and objectives and in addition to this comparison of data placement schemes.
 
 Parminder Singh et al. presented a comprehensive review of Auto-Scaling techniques of web applications in cloud computing. The complete taxonomy of the reviewed articles is done on varied parameters like auto-scaling, approach, resources, monitoring tool, experiment, workload and metric, etc.
 
 Simar Preet Singh et al. in the paper titled “Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platform” proposed a novel scheme to improve the user contentment by improving the cost to operation length ratio, reducing the customer churn, and boosting the operational revenue. The proposed scheme is learnt to reduce the queue size by effectively allocating the resources, which resulted in the form of quicker completion of user workflows. The proposed method results are evaluated against the state-of-the-art scene with non-power aware based task scheduling mechanism. The results were analyzed using parameters-- energy, SLA infringement and workflow execution delay. The performance of the proposed schema was analyzed in various experiments particularly designed to analyze various aspects for workflow processing on given fog resources. The LRR (35.85 kWh) model has been found most efficient on the basis of average energy consumption in comparison to the LR (34.86 kWh), THR (41.97 kWh), MAD (45.73 kWh) and IQR (47.87 kWh). The LRR model has been also observed as the leader when compared on the basis of number of VM migrations. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

Over the past few years, the cloud based storage providers such as Google drive, OneDrive, Box and Dropbox are increasingly being used to store users personal profile based data, which users could share over cloud storage devices. Immediately, the users data leaves the user premises and enters the cloud storage, the user has lost all the control over its data, and even when that data is further shared for the purposes of collaboration. Digital rights management (DRM) for Content hosted in Cloud based storage covers technologies related devices that protects the digital assets stored in cloud by enforcing the permission regulator, and when the data is in collaboration. This is important to protect healthcare (Fiaz, M., et al., 2019. Social media news classification in healthcare communication. Journal of Medical Imaging and Health Informatics, 9(6), pp.1215–1223; Irfan, R., et al., 2019. Ontology learning in text mining for handling big data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9(4), pp.649–661; Ikram Ullah, L. and Ali Shariq, I., 2019. A special section on effectiveness of search engine and social networking data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9(4), pp.647–648; Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9(7), pp.1337–1345; Shahzad, B., et al., 2017. Finding healthcare issues with search engine queries and social network data. International Journal of Semantic Web and Information System, 13(1), pp.48–62; Saleem, K., et al., 2015. Human-oriented design of secure machine-to-machine communication system for e-Healthcare society. Computers in Human Behaviour, 51, pp.977–985.) data from being misused by authorized and unauthorized entities. To implement DRM in cloud, these Authors have proposed encryption based solution. Due to the distributed nature of cloud it is suitable for the real time data transportation. However, DRM process requires performance that is close to real time. This paper summarized the current state of the research in Digital Rights Management and leveraged Fog computing in DRM. This is to meet the new performance requirements for next-generation distribution networks, in terms of performance metrics, such as energy consumption, traffic delay, and network throughput, as required by in large and heterogonous Cloud solutions (Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9(7), pp.1337–1345.) as data storage system.

Cette thèse porte sur le Web Sémantique des Objets (WSdO), un domaine de recherche à l'interface de l'Internet des Objets (IdO) et du Web Sémantique (WS). L’intégration des approche du WS à l'IdO permettent de traiter l'importante hétérogénéité des ressources, des technologies et des applications de l'IdO, laquelle est une source de problèmes d'interopérabilité freinant le déploiement de systèmes IdO. Un premier verrou scientifique est lié à la consommation en ressource des technologies du WS, là où l'IdO s’appuie sur des objets aux capacités de calcul et de communication limitées. De plus, les réseaux IdO sont déployés à grande échelle, quand la montée en charge est difficile pour les technologies du WS. Cette thèse a pour objectif de traiter ce double défi, et comporte deux contributions. La première porte sur l'identification de critères de qualité pour les ontologies de l'IdO, et l’élaboration de IoT-O, une ontologie modulaire pour l'IdO. IoT-O a été implantée pour enrichir les données d'un bâtiment instrumenté, et pour être moteur de semIoTics, notre application de gestion autonomique. La seconde contribution est EDR (Emergent Distributed Reasoning), une approche générique pour distribuer dynamiquement le raisonnement à base de règles. Les règles sont propagées de proche en proche en s'appuyant sur les descriptions échangées entre noeuds. EDR est évaluée dans deux scénario concrets, s'appuyant sur un serveur et des noeuds contraints pour simuler le déploiement<br>This thesis is situated in the Semantic Web of things (SWoT) domain, at the interface between the Internet of Things (IoT) and the Semantic Web (SW). The integration of SW approaches into the IoT aim at tackling the important heterogeneity of resources, technologies and applications in the IoT, which creates interoperability issues impeding the deployment of IoT systems. A first scientific challenge is risen by the resource consumption of the SW technologies, inadequated to the limites computation and communication capabilities of IoT devices. Moreover, IoT networks are deployed at a large scale, when SW technologies have scalability issues. This thesis addresses this double challenge by two contributions. The first one is the identification of quality criteria for IoT ontologies, leading to the proposition of IoT-O, a modular IoT ontology. IoT-O is deployed to enrich data from a smart building, and drive semIoTics, our autonomic computing application. The second contribution is EDR (Emergent Distributed Reasoning), a generic approach to dynamically distributed rule-based reasoning. Rules are propagated peer-to-peer, guided by descriptions exchanged among nodes. EDR is evaluated in two use-cases, using both a server and some constrained nodes to simulate the deployment

In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.

Spreading activation is a common method for searching semantic or neural networks, it iteratively propagates activation for one or more sources through a network { a process that is computationally intensive. Spectral association is a recent technique to approximate spreading activation in one go, and therefore provides very fast computation of activation levels. In this paper we evaluate the characteristics of spectral association as replacement for classic spreading activation in the domain of ontology learning. The evaluation focuses on run-time performance measures of our implementation of both methods for various network sizes. Furthermore, we investigate differences in output, i.e. the resulting ontologies, between spreading activation and spectral association. The experiments confirm an excessive speedup in the computation of activation levels, and also a fast calculation of the spectral association operator if using a variant we called brute force. The paper concludes with pros and cons and usage recommendations for the methods. (authors' abstract)

The number of R extension packages available from the CRAN repository has tremendously grown over the past 10 years. We look at this phenomenon in more detail, and discuss some of its consequences. In particular, we argue that the statistical computing community needs a more common understanding of software quality, and better domain-specific semantic resources.

Yes<br>The use of Information and Communications Technology (ICTs) in healthcare has the potential of minimizing medical errors, reducing healthcare cost and improving collaboration between healthcare systems which can dramatically improve the healthcare service quality. However interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a lack of collaboration and exchange of healthcare information. To solve this problem, cross healthcare system collaboration is required. This paper proposes a conceptual semantic based healthcare collaboration framework based on Internet of Things (IoT) infrastructure that is able to offer a secure cross system information and knowledge exchange between different healthcare systems seamlessly that is readable by both machines and humans. In the proposed framework, an intelligent semantic gateway is introduced where a web application with restful Application Programming Interface (API) is used to expose the healthcare information of each system for collaboration. A case study that exposed the patient's data between two different healthcare systems was practically demonstrated where a pharmacist can access the patient's electronic prescription from the clinic.<br>British Council Institutional Links grant under the BEIS-managed Newton Fund.

Quipper is a domain-specific programming language for the description of quantum circuits. Because it is implemented as an embedded language in Haskell, Quipper is a very practical functional language. However, for the same reason, it lacks a formal semantics and it is limited by Haskell's type-system. In particular, because Haskell lacks linear types, it is easy to write Quipper programs that violate the non-cloning property of quantum states. In order to formalize relevant fragments of Quipper in a type-safe way, the Proto-Quipper family of research languages has been introduced over the last years. In this thesis we first introduce Quipper and Proto-Quipper-M. Proto-Quipper-M is an instance of the Proto-Quipper family based on a categorical model for quantum circuits, which features a linear type-system that guarantees that the non-cloning property holds at compile time. We then derive a tentative small-step operational semantics from the big-step semantics of Proto-Quipper-M and we prove that the two are equivalent. After proving subject reduction and progress results for the tentative semantics, we build upon it to obtain a truly small-step semantics in the style of an abstract machine, which we eventually prove to be equivalent to the original semantics.

Modern day mobile robots are constrained in the resources available to them. Only so much hardware can be fit onto the robotic frame and at the same time they are required to perform tasks that require lots of computational resources, access to massive amounts of data and the ability to share knowledge with other robots around it. This thesis explores the cloud robotics approach in which complex compu- tations can be offloaded to a cloud service which can have a huge amount of computational resources and access to massive data sets. The Robot Operat- ing System, ROS, is extended to allow the robot to communicate with a high powered cluster and this system is used to test our approach on such a complex task as semantic scene understanding. The benefits of the cloud approach is utilized to connect to a cloud based object detection system and to build a cat- egorization system relying on large scale datasets and a parallel computation model. Finally a method is proposed for building a consistent scene description by exploiting semantic relationships between objects.<br>Moderna mobila robotar har begränsade resurser. Det får inte plats hur mycket hårdvara som helst på roboten och ändå förväntas de utföra arbeten som kräver extremt mycket datorkraft, tillgång till enorm mängd data och samtidigt kommunicera med andra robotar runt omkring sig. Det här examensarbetet utforskar robotik i molnet där komplexa beräk- ningar kan läggas ut i en molntjänst som kan ha tillgång till denna stora mängd datakraft och ha plats för de stora datamängder som behövs. The Ro- bot Operating System, eller ROS, byggs ut för att stödja kommunikation med en molntjänst och det här systemet används sedan för att testa vår lösning på ett så komplext problem som att förstå en omgivning eller miljö på ett seman- tiskt plan. Fördelarna med att använda en molnbaserad lösning används genom att koppla upp sig mot ett objektigenkänningssytem i molnet och för att byg- ga ett objektkategoriseringssystem som förlitar sig på storskaliga datamängder och parallella beräkningsmodeller. Slutligen föreslås en metod för att bygga en tillförlitlig miljöbeskrivning genom att utnyttja semantiska relationer mellan föremål.

Image retrieval via a structured query is explored in Johnson, et al. [7]. The query is structured as a scene graph and a graphical model is generated from the scene graph's object, attribute, and relationship structure. Inference is performed on the graphical model with candidate images and the energy results are used to rank the best matches. In [7], scene graph objects that are not in the set of recognized objects are not represented in the graphical model. This work proposes and tests two approaches for modeling the unrecognized objects in order to leverage the attribute and relationship models to improve image retrieval performance.

We analyse the semantics of aspect-oriented extensions to functional languages by presenting compositional translations of these primitives into languages with traditional notions of state and control. As a first step, we examine an existing semantic description of aspects which allows the labelling of program points. We show that a restriction of these semantics to aspects which do not preempt the execution of code can be fully abstractly translated into a functional calculus with higher order references, but that removing this restriction requires a notion of exception handling to be added to the target language in order to yield a sound semantics. Next, we proceed to show that abandoning the labelling technique, and consequently relaxing the so-called ``obliviousness'' property of aspectual languages, allows preemptive aspects to be included in the general references model without the need for exceptions. This means that the game model of general references is inherited by the aspect calculus. The net result is a clean semantic description of aspect-orientation, which mirrors recently published techniques for their implementation, and thereby provides theoretical justification for these systems. The practical validity of our semantics is demonstrated by implementing extensions to the basic calculus in Standard ML, and showing how a number of useful aspect-oriented features can be expressed using general references alone. Our theoretical methodology closely follows the proof structure that often appears in the game semantics literature, and therefore provides an operational perspective on notions such as ``bad variables'' and factorisation theorems.

The paper presents a method for computing indexes of semantic concreteness and abstractness in two languages (Russian and English). These indexes are used in metaphor identification experiments in both languages; the results are either comparable to or surpass pervious work and the baselines. We analyze the obtained indexes of concreteness and abstractness to see how they align with the linguistic intuitions about the corresponding semantic categories. The results of the analysis may have broader implications for computational studies of the semantics of concreteness and abstractness.

Product Lifecycle Management (PLM) is a concept that takes into account that the development of a product is influenced by knowledge from various stakeholders throughout its lifecycle. Computing environments in the PLM framework are expected to have several independent information resources. This requires a meaningful formal representation of product data semantics throughout the product’s lifecycle. This paper presents an ontological approach to formalize product semantics into a Product Semantic Representation Language (PSRL). Building blocks to develop the explicit, extensible and comprehensive PSRL are described. The PSRL is open and based on standard W3L OWL constructs. The extensibility is demonstrated by considering an example product. The representation and the method of its development is expected to support several applications in the context of PLM. The use of OWL will enable the provision of the application software and information resources as Web services in the context of the Semantic Web.

We study the problem of associating formal semantic descriptions to data services. We base our proposal on the Ontology-based Data Access paradigm, where a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on the notion of source-to-ontology (s-to-o) rewriting, which comes in three variants, called sound, complete and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an s-to-o rewriting of a given data service), and computation (computing an s-to-o rewriting of a data service).

We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking (in the backdrop of autonomous driving). A general method for online visual sensemaking using answer set programming is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework usable within hybrid architectures for perception &amp; control. We evaluate and demo with community established benchmarks KITTIMOD and MOT. As use-case, we focus on the significance of human-centred visual sensemaking ---e.g., semantic representation and explainability, question-answering, commonsense interpolation--- in safety-critical autonomous driving situations.

This paper is devoted to the development of a methodology for evaluating the semantic similarity of any texts in different languages is developed. The study is based on the hypothesis that the proximity of vector representations of terms in semantic space can be interpreted as a semantic similarity in the cross-lingual environment. Each text will be associated with a vector in a single multilingual semantic vector space. The measure of the semantic similarity of texts will be determined by the measure of the proximity of the corresponding vectors. We propose a quantitative indicator called Index of Semantic Textual Similarity (ISTS) that measures the degree of semantic similarity of multilingual texts on the basis of identified cross-lingual semantic implicit links. The setting of parameters is based on the correlation with the presence of a formal reference between documents. The measure of semantic similarity expresses the existence of two common terms, phrases or word combinations. Optimal parameters of the algorithm for identifying implicit links are selected on the thematic collection by maximizing the correlation of explicit and implicit connections. The developed algorithm can facilitate the search for close documents in the analysis of multilingual patent documentation.

The project aims to solve the fundamental scientific problem of semantic modeling, within the framework of which a methodology is developed for the automated identification of translation links (translation correspondences), as well as hierarchical, synonymous and associative links from Internet texts and the construction of multilingual associative hierarchical portraits of subject area (MAHPSA), in particular, on autonomous uninhabited underwater vehicles (UUV). Accounting for multilingual and heterogeneous resources allows you to get a more complete picture of what is happening in the subject area, to identify the sources of the origin of ideas, the speed and directions of their distribution, to identify significant documents and promising directions. The solution to the problem is based on an integrated approach that combines the methods of statistics, corpus linguistics and distributive semantics, and is implemented in technology that involves the development of linguo-statistical mechanisms for the formation of a multilingual associative hierarchical portrait of a subject area, which is a dictionary of significant terms of the subject area, the elements of which organized in synonymous series (synsets), including translational correspondences, as well as associative and hierarchical relationships.