Virtual reality (VR) provides many exciting new application opportunities, but also present new challenges. In contrast to 360° videos that only allow a user to select its viewing direction, in fully immersive VR, users can also move around and interact with objects in the virtual world. To most effectively deliver such services it is therefore important to understand how users move around in relation to such objects. In this paper, we present a methodology and software tool for generating run-time datasets capturing a user’s interactions with such 3D environments, evaluate and compare different object identification methods that we implement within the tool, and use datasets collected with the tool to demonstrate example uses. The tool was developed in Unity, easily integrates with existing Unity applications through the use of periodic calls that extracts information about the environment using different ray-casting methods. The software tool and example datasets are made available with this paper.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants.This paper is an overall presentation of that campaign.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus).The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by18 participants. This paper is an overall presentation of that campaign
Ontologies have been proposed as a means towards making data FAIR (Findable, Accessible, Interoperable, Reusable). This has attracted much interest in several communities and ontologies are being developed. However, to obtain good results when using ontologies in semantically-enabled applications, the ontologies need to be of high quality. One of the quality aspects is that the ontologies should be as complete as possible. In this paper we propose a first version of a tool that supports users in extending ontologies using a phrase-based approach. To demonstrate the usefulness of our proposed tool, we exemplify the use by extending the Materials Design Ontology.
Ontologies have been proposed as a means towards making data FAIR (Findable, Accessible, Interoperable, Reusable) and has recently attracted much interest in the materials science community. Ontologies for this domain are being developed and one such effort is the Materials Design Ontology. However, to obtain good results when using ontologies in semantically-enabled applications, the ontologies need to be of high quality. One of the quality aspects is that the ontologies should be as complete as possible. In this paper we show preliminary results regarding extending the Materials Design Ontology using a phrase-based topic model.
Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology.
Despite the X509 public key infrastructure (PKI) being essential for ensuring the trust we place in our communication with web servers, the revocation of the trust placed in individual X509 certificates is neither transparent nor well-studied, leaving many unanswered questions. In this paper, we present a temporal analysis of 36 million certificates, whose revocation statuses we followed for 120 days since first being issued. We characterize the revocation rates of different certificate authorities (CAs) and how the rates change over the lifetime of the certificates. We identify and discuss several instances where the status changes from "revoked" to "good", "unauthorized" or "unknown", respectively, before the certificates expiry. This complements prior work that has observed such inconsistencies in some CAs behavior after expiry but also highlight a potentially more severe problem. Our results highlight heterogeneous revocation practices among the CAs.
A trend seen on the web today is to create a platform where externally developed applications can run inside some kind of main application. This is often done by providing an API to access data and business logic of your service and a sandbox environment in which third-party applications can run. By providing this, it is made possible for external developers to come up with new ideas based on your service. Some good examples on this are Spotify Apps, Apps on Facebook and SalesForce.com.
Ipendo Systems AB is a company that develops a web platform for intellectual properties. Currently most things on this platform are developed by developers at Ipendo Systems AB. Some interest has though risen to enable external developers to create applications that will in some way run inside the main platform.
In this thesis an analysis of already existing solutions has been done. These solutions were Spotify Apps and Apps on Facebook. The two have different approaches on how to enable third-party applications to run inside their own service. Facebook’s solution builds mainly on iframe embedded web pages where data access is provided through a web API. Spotify on the other hand hosts the third-party applications themselves but the applications may only consist of HTML5, CSS3 and JavaScript.
In addition to the analysis a prototype was developed. The purpose of the prototype was to show possible ways to enable third-party applications to run inside your own service. Two solutions showing this were developed. The first one was based on Facebook’s approach with iframing of external web pages. The second was a slightly modified version of Spotify’s solution with only client-side code hosted by the main application. To safely embed the client side code in the main application a sandboxing tool for JavaScript called Caja was used.
Of the two versions implemented in the prototype was the Iframe solution considered more ready to be utilized in a production environment than Caja. Caja could be seen as an interesting technique for the future but might not be ready to use today. The reason behind this conclusion was that Caja decreased the performance of the written JavaScript as well as adding complexity while developing the third-party applications.
Federated RDF query processing is concerned with querying a federation of RDF data sources where the queries are expressed using a declarative query language (typically, the RDF query language SPARQL), and the data sources are autonomous and heterogeneous. The current literature in this context assumes that the data and the data sources are semantically homogeneous, while heterogeneity occurs at the level of data formats and access protocols.
When deploying software engineering applications in the cloud there are two similar software components used. These are Virtual Machines and Containers. In recent years containers have seen an increase in popularity and usage, in part because of tools such as Docker and Kubernetes. Virtual Machines (VM) have also seen an increase in usage as more companies move to solutions in the cloud with services like Amazon Web Services, Google Compute Engine, Microsoft Azure and DigitalOcean. There are also some solutions using auto-scaling, a technique where VMs are commisioned and deployed to as load increases in order to increase application performace. As the application load decreases VMs are decommisioned to reduce costs.
In this thesis we implement and evaluate auto-scaling policies that use both Virtual Machines and Containers. We compare four different policies, including two baseline policies. For the non-baseline policies we define a policy where we use a single Container for every Virtual Machine and a policy where we use several Containers per Virtual Machine. To compare the policies we deploy an image serving application and run workloads to test them. We find that the choice of deployment strategy and policy matters for response time and error rate. We also find that deploying applications as described in the methodis estimated to take roughly 2 to 3 minutes.
We study a multiuser system in which an information source provides status updates to two monitors with heterogeneous goals. Semantic filtering is first performed to select the most useful realizations for each monitor. Packets are then encoded and sent so that each monitor can timely fulfill its goal. In this regard, some realizations are important for both monitors, while every other realization is informative for only one monitor. We determine the optimal real codeword lengths assigned to the selected packet arrivals in the sense of maximizing a weighted sum of semantics-aware utility functions for the two monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using semantic filtering and source coding.
Most of the people in the industrial world are using several web applications every day. Many of those web applications contain vulnerabilities that can allow attackers to steal sensitive data from the web application's users. One way to detect these vulnerabilities is to have a penetration tester examine the web application. A common way to train penetration testers to find vulnerabilities is to challenge them with realistic web applications that contain vulnerabilities. The penetration tester's assignment is to try to locate and exploit the vulnerabilities in the web application. Training on the same web application twice will not provide any new challenges to the penetration tester, because the penetration tester already knows how to exploit all the vulnerabilities in the web application. Therefore, a vast number of web applications and variants of web applications are needed to train on.
This thesis describes a tool designed and developed to automatically generate vulnerable web applications. First a web application is prepared, so that the tool can generate a vulnerable version of the web application. The tool injects Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) vulnerabilities in prepared web applications. Different variations of the same vulnerability can also be injected, so that different methods are needed to exploit the vulnerability depending on the variation. A purpose of the tool is that it should generate web applications which shall be used to train penetration testers, and some of the vulnerabilities the tool can inject, cannot be detected by current free web application vulnerability scanners, and would thus need to be detected by a penetration tester.
To inject the vulnerabilities, the tool uses abstract syntax trees and taint analysis to detect where vulnerabilities can be injected in the prepared web applications.
Tests confirm that web application vulnerability scanners cannot find all the vulnerabilities on the web applications which have been generated by the tool.
The global engineering design house Syntronic has requested a further development of the open source Python framework PyVISA-sim to enable dynamic simulations of signals and measuring instruments which would streamline development of their internal radio equipment testing tool. This tool is used by a world leading telecommunications company when developing their next generation radio equipment. PyVISA-sim is used in lab environments to test applications without access to real connected instruments. The project detailed in this thesis strives to pinpoint Syntronic’s needs, develop the requested functionality within the framework and have the changes implemented as part of the official GitHub repository, thereby making them available for anyone wanting to utilize them. To achieve this agile software development methods are utilized combined with an open source mindset. The resulting additions to PyVISA-sim can reduce the workload for all users in need of a more complex simulation method.
5G will provide broadband access everywhere, entertain higher user mobility, and enable connectivity of massive number of devices (e.g. Internet of Things (IoT)) in an ultrareliable and affordable way. The main technological enablers such as cloud computing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) are maturing towards their use in 5G. However, there are pressing security challenges in these technologies besides the growing concerns for user privacy. In this paper, we provide an overview of the security challenges in these technologies and the issues of privacy in 5G. Furthermore, we present security solutions to these challenges and future directions for secure 5G systems.
Host Identity Protocol (HIP), a novel internetworking technology proposes separation of the identity-location roles of the Internet Protocol (IP). HIP has been successful from the technological perspectives for network security and mobility, however, it has very limited deployment. In this paper we assess HIP to find the reasons behind its limited deployment and highlight the challenges faced by HIP for its commercial use. We propose technological development and outline deployment strategies for the wide use of HIP. Furthermore, this paper investigates the use of HIP in Software Defined Networks (SDN) to evaluate its performance in new disruptive networking technologies. In a nutshell, this paper presents revealing challenges for the deployment of innovative networking protocols and a way ahead for successful and large scale deployment.
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.
The integration of satellite and terrestrial networks has become inevitable in the next generations of communications networks due to emerging needs of ubiquitous connectivity of remote locations. New and existing services and critical infrastructures in remote locations in sea, on land and in space will be seamlessly connected through a diverse set of terrestrial and non-terrestrial communication technologies. However, the integration of terrestrial and non-terrestrial systems will open up both systems to unique security challenges that can arise due to the migration of security challenges from one to another. Similarly, security challenges can also arise due to the incompatibility of distinct systems or incoherence of security policies. The resulting security implications, thus, can be highly consequential due to the criticality of the infrastructures such as space stations, autonomous ships, and airplanes, for instance. Therefore, in this article we study existing security challenges in satellite-terrestrial communication systems and discuss potential solutions for those challenges. Furthermore, we provide important research directions to encourage future research on existing security gaps.
The development of the Fifth Generation (5G) wireless networks is gaining momentum to connect almost all aspects of life through the network with much higher speed, very low latency and ubiquitous connectivity. Due to its crucial role in our lives, the network must secure its users, components, and services. The security threat landscape of 5G has grown enormously due to the unprecedented increase in types of services and in the number of devices. Therefore, security solutions if not developed yet must be envisioned already to cope with diverse threats on various services, novel technologies, and increased user information accessible by the network. This article outlines the 5G network threat landscape, the security vulnerabilities in the new technological concepts that will be adopted by 5G, and provides either solutions to those threats or future directions to cope with those security challenges. We also provide a brief outline of the post-5G cellular technologies and their security vulnerabilities which is referred to as Future Generations (XG) in this paper. In brief, this article highlights the present and future security challenges in wireless networks, mainly in 5G, and future directions to secure wireless networks beyond 5G.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2018 campaign offered 12 tracks with 23 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation,open evaluation, or consensus). The OAEI2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign.
In this position paper we briefly introduce SmartEnv ontology which relies on SEmantic Sensor Network (SSN) ontology and is used to represent different aspects of smart and sensorized environments. We will also talk about E-carehome project aiming at providing an IoT-based health-care system for elderly people at their homes. Furthermore, we refer to the role of SmartEnv in Ecarehome and how it needs to be further extended to achieve semantic interoperability as one of the challenges in development of autonomous health care systems at home.
With 360 degrees video, only a limited fraction of the full view is displayed at each point in time. This has prompted the design of streaming delivery techniques that allow alternative playback qualities to be delivered for each candidate viewing direction. However, while prefetching based on the users expected viewing direction is best done close to playback deadlines, large buffers are needed to protect against shortfalls in future available bandwidth. This results in conflicting goals and an important prefetch aggressiveness tradeoff problem regarding how far ahead in time from the current playpoint prefetching should be done. This paper presents the first characterization of this tradeoff. The main contributions include an empirical characterization of head movement behavior based on data from viewing sessions of four different categories of 360 degrees video, an optimization-based comparison of the prefetch aggressiveness tradeoffs seen for these video categories, and a data-driven discussion of further optimizations, which include a novel system design that allows both tradeoff objectives to be targeted simultaneously. By qualitatively and quantitatively analyzing the above tradeoffs, we provide insights into how to best design tomorrows delivery systems for 360 degrees videos, allowing content providers to reduce bandwidth costs and improve users playback experiences.
Sectra has a customer database with approximately 1600 customers across the world. In this system there exists not only medical information but alsoinformation about the environment which the system runs in, usage pattern and much more.
This report is about storing data received from log les into a suitable database. Sectra wants to be able to analyze this information so that they can make strategic decisions and get a better understanding of their customers' needs. The tested databases are MongoDB, Cassandra, and MySQL. The results shows that MySQL is not suitable for storing large amount of data with the current conguration. On the other hand, both MongoDB and Cassandra performed well with the growing amount of data.
Security has become a necessary part of nearly every software development project, as the overall risk from malicious users is constantly increasing, due to increased consequences of failure, security threats and exposure to threats. There are few projects today where software security can be ignored. Despite this, security is still rarely taken into account throughout the entire software lifecycle; security is often an afterthought, bolted on late in development, with little thought to what threats and exposures exist. Little thought is given to maintaining security in the face of evolving threats and exposures. Software developers are usually not security experts. However, there are methods and tools available today that can help developers build more secure software. Security modeling, modeling of e.g., threats and vulnerabilities, is one such method that, when integrated in the software development process, can help developers prevent security problems in software. We discuss these issues, and present how modeling tools, vulnerability repositories and development tools can be connected to provide support for secure software development
Security is often an afterthought when developing software, and is often bolted on late in development or even during deployment or maintenance, through activities such as penetration testing, add-on security software and penetrate-and patch maintenance. We believe that security needs to be built in to the software from the beginning, and that security activities need to take place throughout the software lifecycle. Accomplishing this effectively and efficiently requires structured approach combining a detailed understanding on what causes vulnerabilities, and how specific activities combine to prevent them.In this paper we introduce key elements of the approach we are taking: vulnerability cause graphs, which encode information about vulnerability causes, and security activity graphs, which encode information about security activities. We discuss how these can be applied to design software development processes (or changes to processes) that eliminate software vulnerabilities.
Incident post-mortem analysis after recovery from incidents is recommended by most incident response experts. An analysis of why and how an incident happened is crucial for determining appropriate countermeasures to prevent the recurrence of the incident. Currently, there is a lack of structured methods for such an analysis, which would identify the causes of a security incident. In this paper, we present a structured method to perform the post-mortem analysis and to model the causes of an incident visually in a graph structure. This method is an extension of our earlier work on modeling software vulnerabilities. The goal of modeling incidents is to develop an understanding of what could have caused the security incident and how its recurrence can be prevented in the future. The method presented in this paper is intended to be used during the post-mortem analysis of incidents by incident response teams.
In this paper we present a security plug-in for the OpenUP/Basic development process. Our security plug-in is based on a structured unified process for secure software development, named S3P (sustainable software security process). This process provides the formalism required to identify the causes of vulnerabilities and the mitigation techniques that prevent these vulnerabilities. We also present the results of an expert evaluation of the security plug-in. The lessons learned from development of the plug-in and the results of the evaluation will be used when adapting S3P to other software development processes.
Security of software systems has become one of the biggest concerns in our everyday life, since software systems are increasingly used by individuals, companies and governments. One way to help software system consumers gain assurance about the security measures of software products is to evaluate and certify these products with standard evaluation processes. The Common Criteria (ISO/IEC 15408) evaluation scheme is a standard that is widely used by software vendors. This process does not include information about already known vulnerabilities, their attack data and lessons learned from them. This has resulted in criticisms concerning the accuracy of this evaluation scheme since it might not address the areas in which actual vulnerabilities might occur.
In this paper, we present a methodology that introduces information about threats from vulnerabilities to Common Criteria documents. Our methodology improves the accuracy of the Common Criteria by providing information about known vulnerabilities in Common Criteria’s security target. Our methodology also provides documentation about how to fulfill certain security requirements, which can reduce the time for evaluation of the products.
Timely and accurate flow classification is important for identifying flows with different service requirements, optimized network management, and for helping network operators simultaneously operate networks at higher utilization while providing end users good quality of experience (QoE). With most services starting to use end-to-end encryption (HTTPS and QUIC), traditional Deep Packet Inspection (DPI) and port-based approaches are no longer applicable. Furthermore, most flow-level-based approaches ignore the complex non-linear characteristics of internet traffic (e.g., self similarity). To address this challenge, in this paper, we present and evaluate a classification framework that combines multi-fractal feature extraction based on time series data (which captures these non-linear characteristics), principal component analysis (PCA) based feature selection, and man-in-the-middle (MITM) based flow labeling. Our detailed evaluation shows that the method is able to quickly and effectively classify traffic belonging to the six most popular traffic types (video streaming, web browsing, social networking, audio communication, text communication, and bulk download) and to distinguish between video-on-demand (VoD) and live streaming sessions delivered from the same services. Our results show that good accuracy can be achieved with only information about the timing of the packets within a flow.
here is a continuous struggle for control of resources at every organization that is connected to the Internet. The local organization wishes to use its resources to achieve strategic goals. Some external entities seek direct control of these resources, for purposes such as spamming or launching denial-of-service attacks. Other external entities seek indirect control of assets (e. g., users, finances), but provide services in exchange for them. less thanbrgreater than less thanbrgreater thanUsing a year-long trace from an edge network, we examine what various external organizations know about one organization. We compare the types of information exposed by or to external organizations using either active (reconnaissance) or passive (surveillance) techniques. We also explore the direct and indirect control external entities have on local IT resources.
The World Wide Web and the services it provides are continually evolving. Even for a single time instant, it is a complex task to methodologically determine the infrastructure over which these services are provided and the corresponding effect on user perceived performance. For such tasks, researchers typically rely on active measurements or large numbers of volunteer users. In this paper, we consider an alternative approach, which we refer to as passive crowd-based monitoring. More specifically, we use passively collected proxy logs from a global enterprise to observe differences in the quality of service (QoS) experienced by users on different continents. We also show how this technique can measure properties of the underlying infrastructures of different Web content providers. While some of these properties have been observed using active measurements, we are the first to show that many of these properties (such as location of servers) can be obtained using passive measurements of actual user activity. Passive crowd-based monitoring has the advantages that it does not add any overhead on Web infrastructure, it does not require any specific software on the clients, but still captures the performance and infrastructure observed by actual Web usage.
Congestion-aware scheduling in the case of traditional downlink cellular communication has neglected the heterogeneity in terms of secrecy among different clients. In this paper, we study a two-user congestion-aware broadcast channel with heterogeneous traffic and different security requirements. The traffic with security requirements is intended for a legitimate user and it has bursty nature. The incoming packets are stored in a queue at the source. Furthermore, there is a second traffic flow intended for another user, it is delay tolerant and does not have secrecy constraints. The receiver which needs to be served with confidential data has full-duplex capabilities, and it can send a jamming signal to hinder eavesdropping of its data at the other user. We consider two randomized policies for selecting which packets to transmit, one is congestion-aware by taking into consideration the queue size, whereas the other one is non-congestion-aware. We analyse the throughput and the delay performance under two decoding schemes at the receivers and provide insights into their relative security performance and into how congestion control at the queue holding confidential information can help decrease the average delay per packet. We show that the two policies have the same secrecy performance for large random access probabilities. The derived results also take account of the self-interference caused at the receiver for whom confidential data is intended due to its full-duplex operation while jamming the communication at the other user.
Congestion-aware scheduling in case of downlink cellular communication has ignored the distribution of diverse content to different clients with heterogeneous secrecy requirements. Other possible application areas that encounter the preceding issue are secure offloading in mobile-edge computing, and vehicular communication. In this paper, we extend the work in Arvanitaki et al. (SN Comput Sci 1(1):53, 2019) by taking into consideration congestion and random access. Specifically, we study a two-user congestion-aware broadcast channel with heterogeneous traffic and different security requirements. We consider two randomized policies for selecting which packets to transmit, one is congestion-aware by taking into consideration the queue size, whereas the other one is congestion-agnostic. We analyse the throughput and the delay performance under two decoding schemes at the receivers, and provide insights into their relative security performance and into how congestion control at the queue holding confidential information can help decrease the average delay per packet. We show that the congestion-aware policy provides better delay, throughput, and secrecy performance for large arrival packet probabilities at the queue holding the confidential information. The derived results also take account of the self-interference caused at the receiver for whom confidential data is intended due to its full-duplex operation while jamming the communication at the other user. Finally, for two decoding schemes, we formulate our problems in terms of multi-objective optimization, which allows for finding a trade-off between the average packet delay for packets intended for the legitimate user and the throughput for the other user under congestion-aware policy.
In this paper we consider the two-user broadcast channel with security constraints. We assume that one of the receivers has a secrecy constraint; i.e., its packets need to be kept secret from the other receiver. The receiver with secrecy constraint has full-duplex capability to transmit a jamming signal to increase its secrecy. We derive the average delay per packet and provide simulation and numerical results, where we compare different performance metrics for the cases when the legitimate receiver performs successive decoding and when both receivers treat interference as noise.
In this paper, we consider the two-user broadcast channel with security constraints. We assume that a source broadcasts packets to two receivers, and that one of them has secrecy constraints, i.e., its packets need to be kept secret from the other receiver. The receiver with secrecy constraint has full-duplex capability, allowing it to transmit a jamming signal to increase its secrecy. We derive the average delay per packet and provide simulations and numerical results, where we compare different performance metrics for the cases when both receivers treat interference as noise, when the legitimate receiver performs successive decoding, and when the eavesdropper performs successive decoding. The results show that successive decoding provides better average packet delay for the legitimate user. Furthermore, we define a new metric that characterizes the reduction on the success probability for the legitimate user that is caused by the secrecy constraint. The results show that secrecy poses a significant amount of packet delay for the legitimate receiver when either receiver performs successive decoding. We also formulate an optimization problem, wherein the throughput of the eavesdropper is maximized under delay and secrecy rate constraints at the legitimate receiver. We provide numerical results for the optimization problem, where we show the trade-off between the transmission power for the jamming and the throughput of the non-legitimate receiver. The results provide insights into how channel ordering and encoding differences can be exploited to improve performance under different interference conditions.
Multimedia content streaming from Internet-based sources emerges as one of the most demanded services by wireless users. In order to alleviate excessive traffic due to multimedia content transmission, many architectures (e.g., small cells, femtocells, etc.) have been proposed to offload such traffic to the nearest (or strongest) access point also called "helper". However, the deployment of more helpers is not necessarily beneficial due to their potential of increasing interference. In this work, we evaluate a wireless system which can serve both cacheable and non-cacheable traffic. More specifically, we consider a general system in which a wireless user with limited cache storage requests cacheable content from a data center that can be directly accessed through a base station. The user can be assisted by a pair of wireless helpers that exchange non-cacheable content as well. Files not available from the helpers are transmitted by the base station. We analyze the system throughput and the delay experienced by the cached user and show how these performance metrics are affected by the packet arrival rate at the source helper, the availability of caching helpers, the caches parameters, and the users request rate by means of numerical results.
Monitors affärssystem MONITOR är under ständig utveckling och i och med detta ville man kolla upp huruvida PostgreSQL skulle kunna användas som DBMS istället för det nuvarande; Sybase SQL Anywhere. Examensarbete har därför bestått av en jämförelse hur PostgreSQL står sig jämte andra DBMS:er, en implementering utav en PostgreSQLdatabas som MONITOR arbetar mot samt ett prestandatest utav skapandet av databasen.
I många avseenden verkar PostgreSQL vara ett alternativ till SQL Anywhere;
Dock så är inte PostgreSQL ett bra DBMS att byta till i dagsläget då systemet inte fungerade på grund av att vissa uttryck inte översattes ordentligt samt att ingen motsvarighet till LIST existerar. Ännu större är dock problemet med tiden det tar att flytta data till en PostgreSQL-databas då det inte är intressant att lösa problem med funktioner i systemet om det ändå inte går att använda på grund utav att konvertering av data tar så lång tid som det gör.
Success in the life sciences depends on access to information in knowlegde bases and literature. Finding and extracting the relevant information depends on a user’s domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant queries in response to keyword input from the user. Queries are presented in natural language, translated to formal query syntax and issued to a knowledge base of scientific literature, documents or aligned document segments. We describe the components of the system and exemplify using real-world examples.
Gated Bayesian networks (GBNs) are an extension of Bayesian networks that aim to model systems that have distinct phases. In this paper, we aim to use GBNs to output buy and sell decisions for use in algorithmic trading systems. These systems may have several parameters that require tuning, and assessing the performance of these systems as a function of their parameters cannot be expressed in closed form, and thus requires simulation. Bayesian optimisation has grown in popularity as a means of global optimisation of parameters where the objective function may be costly or a black box. We show how algorithmic trading using GBNs, supported by Bayesian optimisation, can lower risk towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis.
By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.
We propose a regime aware learning algorithm to learn a sequence of Bayesian networks (BNs) that model a system that undergoes regime changes. The last BN in the sequence represents the system’s current regime, and should be used for BN inference. To explore the feasibility of the algorithm, we create baseline tests against learning a singe BN, and show that our proposed algorithm outperforms the single BN approach. We also apply the learning algorithm on real world data from the financial domain, where it is evident that the algorithm is able to produce BNs that have adapted to the regime changes during the most recent global financial crisis of 2007-08.
In this paper we investigate how we can use gated Bayesian networks, a type of probabilistic graphical model, to represent regimes in baseball players’ career data. We find that baseball players do indeed go through different regimes throughout their career, where each regime can be associated with a certain level of performance. We show that some of the transitions between regimes happen in conjunction with major events in the players’ career, such as being traded or injured, but that some transitions cannot be explained by such events. The resulting model is a tool for managers and coaches that can be used to identify where transitions have occurred, as well as an online monitoring tool to detect which regime the player currently is in.