Deep Learning has the ability to train on datasets created from videos. This facility makes deep learning algorithms suitable for detecting distinct objects in large sets of frames, particularly for delineating anomalies like precancerous lesions during surveillance colonoscopies of the large bowel. However, capturing subtle, diffuse characteristics in these videos' frames can be a challenge. This paper presents a deep learning system that uses colour channel separation and recombination of images to improve the performance of an object detection model, to tackle this challenge. Using a dataset from surveillance colonoscopy videos to find precancerous and cancerous lesions in IBD patients, individual colour channels of RGB images in the dataset are separated and recombined to form different datasets which are later used to train YOLOv8x models. The object detection model that is trained and tested with only the blue channel component of the images in the dataset performs better and gives more accurate predictions than the object detection model that is trained and tested with the datasets containing the green and/or the red channel images, as well as the dataset containing the original RGB images.

Detta bidrag beskriver och analyserar förberedelsefasen inför implementeringen av ett högskolepedagogiskt excellensprogram vid Kungliga Tekniska Högskolan, KTH. Programmet syftar till att ytterligare stärka värdet av pedagogiska meriter och samtidigt bidra till lärosätets fortsatta och fördjupade utveckling av utbildningarna och organisationen. De tydligaste riskerna som har identifierats med befintliga pedagogiska meriteringsmodeller är att de kan skapa ett A- och B-lag (mellan en karriär som forskare och en karriär som lärare). Dessutom är det ofta oklart hur personer som har utnämnts till excellenta lärare ska kunna bidra till organisationens och utbildningens utveckling ur ett kortsiktigt såväl som ett långsiktigt perspektiv. En tydlig svaghet med befintliga pedagogiska meriteringsmodeller är att de inte explicit nog ger emfas till aktivt och relevant utvecklingsarbete, utan fokuserar på egenhändigt skrivna pedagogiska portföljer som sällan är framtidsinriktade. KTH:s högskolepedagogiska excellensprogram siktar på att försöka möta dessa risker och svagheter. KTH har, alltjämt sedan det nationella obligatoriet om 15 hp Högskolepedagogik infördes, kvarhållit detta obligatorium. Stora satsningar har gjorts på den högskolepedagogiska verksamheten. Ett gediget utbud av fortbildningsmöjligheter samt arenor för nätverkande har utvecklats. Samtidigt råder svagheter i systemet gällande prövande och tillvaratagande av pedagogisk skicklighet, och det är tydligt att KTH behöver ytterligare utveckling i området. Författarna menar också att i och med att de utmaningar vi står inför gällande pedagogisk meritering ser relativt lika ut vid landets lärosäten, bör ett stärkt nationellt samarbete och nätverkande inom landets ingenjörsutbildningar främjas inom området. Artikelförfattarna representerar KTH:s övergripande ledning, utbildningsledning samt ledningen för den högskolepedagogiska verksamheten.

Information and Communication Technology (ICT)-infrastructures are increasingly important for enabling technology within Smart society with Smart cities and communitieS. An ICT-infrastructure handles data and information and encompasses devices and networks, protocols and procedures including Internet, Internet of Things and Cyber-Physical Systems. The current challenges of ICT-infrastructures are delivering Services and applications that are requested by users, such as residents, public organisations and institutionS. These Services and applications must be combined to enhance and enrich the environment and provide personalised ServiceS. This requires radical changes in technology, such as dynamic ICT-infrastructures, which should dynamically provide requested Services to be able to build Smart societieS. This paper is about pursuing Smart and connected cities and communities by creating Smart volatile ICT-infrastructures for Smart cities and communities, called Ipsum. The infrastructure is of multidisciplinary art and includes different kinds of hardware, software, artificial intelligence techniques depending on the available parts and the Services to be delivered. The goal is to provide a powerful and smart, and cost-saving volatile ICT-infrastructure with person-centred, ubiquitous and malleable parts, i.e., devices, sensors and ServiceS. Volatile means in real-time constitute a volatile network of devices and deploying it into cities and communitieS. Ipsum will be Smart everywhere by collaborating with several different hardware and software Systems and cooperating to perform complex taskS. By including ubiquitous and malleable parts in the infrastructure, Ipsum can facilitate an informed and engaged populace.

Existing authentication protocols for Big Data system such as Apache Hadoop is based on Kerberos. In the Kerberos protocol, there are numerous security issues that have remained unsolved; replay attacks, DDoS and single point of failure are some examples. These indicate potential security vulnerabilities and Big Data risks in using Hadoop. This paper presents drawbacks of Kerberos implementations and identifies authentication requirements that can enhance the security of Big Data in distributed environments. The enhancement proposed is based on the rising technology of blockchain that overcomes shortcomings of Kerberos.

High performance sensor-based systems require distributed methods that quickly release threads and allow hardware to scale as the amount of data increases. Each sensor, component, has to handle a massive amount of data, independently, at the same time it must cooperate with other sensors, components. To facilitate rapid data processing between different components and handle the dataflow simultaneously, knowledge about the components, i.e., metadata, can be incorporated. This paper presents a sensor-based system that applies metadata on internal components and utilizes disaggregation and processing as core services to handle large amount of data from various distributed sensors. By combining components with individual advantages, the system can be designed to allow a high amount of simultaneous data to be disaggregated into the respective categories, which are processed to make information accessible and stored in a database that can easily be accessed by interested parties. To evaluate the system, performance tests have been carried out using metadata on combined components, disaggregation and processing. The tests show that it is possible to build faster and more reliable sensor-based systems that are scalable, fault tolerant and high performing.

The world is moving towards a time where more and more objects, like Internet of Things, will be connected in cyber-physical systems. These objects need excellent tools to collect information from sources in the surrounding environment. By using the wireless communication technologies of modern smartphones, such as Bluetooth, Near Field Communication, and Wi-Fi, a solid ground to transmit and receive information to and from various sources can be established. However, an obstacle communication protocols are needed to make the different devices transmit information. This paper presents a communication protocol for cyber-physical systems using wireless technologies and cloud computing to facilitate information exchange between objects. By constructing a communication protocol and implementing a system on top of the protocol, users can exchange useful information with the help of a smartphone or other devices with similar functionalities in cyber-physical systems. The protocol should enable using services with ease, hiding the complex underlying structure and make interactions as natural as possible.

This paper is a philosophic look at(view of) the promise and perils of cyber physical systems. It is not a doom and gloom view of the systems (It is overview of the current systems and their technologies), we believe in the bright and useful future of the cyber physical environments (systems?). However, looking at the history of technology in the recent past, the need of careful prescription to design systems that prevent the unforeseen less than positive side of technology. No matter how useful a technology is, the providers soon push burdensome "features" on to the unsuspecting user.

Cyber-physical systems are integrations of computerized physical things in the environment that are merged by communications, and computations using embedded systems and networks. To make the components of the systems act intelligently, according to users' needs, and understand and predict behavior of the cyber-physical systems, the cyber-physical systems need reasoning to link the outcome of the different components to be able to make use of the devices in the surrounding environment. The reasoning includes collecting sensor data, finding key concepts in the data and drawing conclusions that cyber-physical systems can use to control the components surrounding the users. However, there is a range of different components in a system where each has particular communication input-output and its own set tasks, which provides particular challenges associated with controlling or predicting the behavior of such systems, which require a kind of analytic tools. This paper presents reasoning strategies for smart cyber-physical systems that can extract and combine data, information, and knowledge to provide an intelligent behavior from users point of view. The reasoning strategies use users' needs as a starting point and provide an environment that gives personalized support.

Ontology integrations are applied for generating new ontologies used for providing advanced services. Although many algorithms and systems for ontology integration have been proposed, it is still very difficult to achieve semantic and pragmatic ontology matching and integration. To tackle the problems, a method of building and using a context knowledge base is needed to get the context of ontologies used for matching and integration. In the method, a context knowledge base with context rules is developed upon an ontology repository to improve the ontology integration. In the context knowledge base, context rules use Ontology Metadata Vocabulary to describe contexts. The stored ontologies that in the repository satisfy a particular context, are extracted and integrated automatically to form contextual information. In the contextual information, both the consistent ontological definitions and the different perspectives from various ontologies of string-identical entities are used. For new ontology integrations, context rules are searched and triggered according to the context of the ontology integration at hand. The contextual information of the rules is aggregated for improving the new ontology integration. Meta-rules are also automatically built to be able to apply the context rules in a hierarchical relation. Experiments of integrations show that the context knowledge base provides extra contextual information for ontology integration. Moreover, when comparing an integration using the context knowledge base to an ontology integration without the context knowledge base, the results of the contextual ontology integrations are improved. At the same time, it is observed that the quantity and the quality of the stored ontologies, in a repository, determine the quality of the context knowledge base. The better quality and quantity of the context knowledge base, the higher degree of improvement it infers to the contextual ontology integrations.