Context: The workload at seaport container terminal is increasing gradually. We need to improve the performance of terminal to fulfill the demand. The key section of the container terminal is container stacking yard which is an integral part of the seaside and the landside. So its performance has the effects on both sides. The main problem in this area is unproductive moves of containers. However, we need a well-planned stacking area in order to increase the performance of terminal and maximum utilization of existing resources.

Objectives: In this work, we have analyzed the existing container stacking system at Helsingborg seaport container terminal, Sweden, investigated the already provided solutions of the problem and find the best optimization technique to get the best possible solution. After this, suggest the solution, test the proposed solution and analyzed the simulation based results with respect to the desired solution.

Methods: To identify the problem, methods and proposed solutions of the given problem in the domain of container stacking yard management, a literature review has been conducted by using some e-resources/databases. A GA with best parametric values is used to get the best optimize solution. A discrete event simulation model for container stacking in the yard has been build and integrated with genetic algorithm. A proposed mathematical model to show the dependency of cost minimization on the number of containers’ moves.

Results: The GA has been achieved the high fitness value versus generations for 150 containers to storage at best location in a block with 3 tier levels and to minimize the unproductive moves in the yard. A comparison between Genetic Algorithm and Tabu Search has been made to verify that the GA has performed better than other algorithm or not. A simulation model with GA has been used to get the simulation based results and to show the container handling by using resources like AGVs, yard crane and delivery trucks and container stacking and retrieval system in the yard. The container stacking cost is directly proportional to the number of moves has been shown by the mathematical model.

Conclusions: We have identified the key factor (unproductive moves) that is the base of other key factors (time & cost) and has an effect on the performance of the stacking yard and overall the whole seaport terminal. We have focused on this drawback of stacking system and proposed a solution that makes this system more efficient. Through this, we can save time and cost both. A Genetic Algorithm is a best approach to solve the unproductive moves problem in container stacking system.

Cassandra is a NoSQL(Not only Structured Query Language) database which serves large amount of data with high availability .Cassandra data storage dimensioning also known as Cassandra capacity planning refers to predicting the amount of disk storage required when a particular product is deployed using Cassandra. This is an important phase in any product development lifecycle involving Cassandra data storage system. The capacity planning is based on many factors which are classified as Cassandra specific and Product specific.This study is to identify the different Cassandra specific and product specific factors affecting the disk space in Cassandra data storage system. Based on these factors a model is to be built which would predict the disk storage for Ericsson’s voucher system.A case-study is conducted on Ericsson’s voucher system and its Cassandra cluster. Interviews were conducted on different Cassandra users within Ericsson R&D to know their opinion on capacity planning approaches and factors affecting disk space for Cassandra. Responses from the interviews were transcribed and analyzed using grounded theory.A total of 9 Cassandra specific factors and 3 product specific factors are identified and documented. Using these 12 factors a model was built. This model was used in predicting the disk space required for voucher system’s Cassandra.The factors affecting disk space for deploying Cassandra are now exhaustively identified. This makes the capacity planning process more efficient. Using these factors the Voucher system’s disk space for deployment is predicted successfully.

Background Requirements engineering and verification (REV) processes play es-sential roles in software product development. There are physical and non-physicaldistances between entities (actors, artifacts, and activities) in these processes. Cur-rent practices that reduce the distances, such as automated testing and alignmentof document structure and tracing only partially close the above mentioned gap.Objective The aim of this thesis is to investigate solutions w.r.t their abilityto reduce the distances between requirements engineering and verification. Twotechniques that are explored in this thesis are automated testing (model-basedtesting, MBT) and alignment of document structure and tracing (traceability).Method The research methods used in this thesis are systematic mapping, soft-ware requirements mining, case study, literature survey, validation study, and de-sign science.Results MBT and traceability are effective in reducing the distance between re-quirements and verification. However, both activities have some shortcoming thatneeds to be addressed when used for that purpose. Current MBT techniques inthe context of software performance do not attain all the goals of MBT: 1) require-ments validation, 2) checking the testability of requirements, and 3) the generationof an efficient test suite. These goals are essential to reduce the distance. We de-veloped and assessed performance requirements verification and test environmentgeneration approach to tackle these shortcomings. Also, traceability between re-quirements and verification suffers from the low granularity of trace links and doesnot support the verification of all requirements. We propose the use of taxonomictrace links to trace and align the structure of requirements specifications and ver-ification artifacts. The results from the validation study show that the solution isfeasible in practice. However, this comes with challenges that need to be addressed.Conclusion MBT and improved traceability reduce multiple distances betweenactors, artifacts, and activities in the requirements engineering and verificationprocess. MBT is most effective in reducing the distances when the model used isbuilt from the requirements. Traceability is essential in easing access to relevantinformation when needed and should not be seen as an overhead. When creatingtrace links, we need to consider the difference in the abstraction, structure, andtime between the linked artifacts

In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Model-Based Testing is a method that supports automated test design by using amodel. Although it is adopted in industrial, it is still an open area within performancerequirements. We aim to look into MBT for performance requirements and find out aframework that can model the performance requirements. We conducted a systematicmapping study, after that we conducted a sample study on software requirementsspecifications, then we introduced the Performance Requirements Verification andValidation (PRVV) model and finally, we completed another sample study to seehow the model works in practice. We found that there are many models can beused for performance requirement while the maturity is not enough. MBT can beimplemented in the context of performance, and it has been gaining momentum inrecent years compared to earlier. The PRVV model we developed can verify theperformance requirements and help to generate the test case.

Model-based testing (MBT) is a method that supports the design and execution of test cases by models that specify theintended behaviors of a system under test. While systematic literature reviews on MBT in general exist, the state of the arton modeling and testing performance requirements has seen much less attention. Therefore, we conducted a systematic map-ping study on model-based performance testing. Then, we studied natural language software requirements specificationsin order to understand which and how performance requirements are typically specified. Since none of the identified MBTtechniques supported a major benefit of modeling, namely identifying faults in requirements specifications, we developed thePerformance Requirements verificatiOn and Test EnvironmentS generaTion approach (PRO-TEST). Finally, we evaluatedPRO-TEST on 149 requirements specifications. We found and analyzed 57 primary studies from the systematic mappingstudy and extracted 50 performance requirements models. However, those models don’t achieve the goals of MBT, whichare validating requirements, ensuring their testability, and generating the minimum required test cases. We analyzed 77 Soft-ware Requirements Specification (SRS) documents, extracted 149 performance requirements from those SRS, and illustratethat with PRO-TEST we can model performance requirements, find issues in those requirements and detect missing ones.We detected three not-quantifiable requirements, 43 not-quantified requirements, and 180 underspecified parameters in the149 modeled performance requirements. Furthermore, we generated 96 test environments from those models. By modelingperformance requirements with PRO-TEST, we can identify issues in the requirements related to their ambiguity, measur-ability, and completeness. Additionally, it allows to generate parameters for test environments

Background: Traceability is an important quality of artifacts that are used in knowledge-intensive tasks. When projectbudgets and time pressure are a reality, this leads often to a down-prioritization of creating trace links. Objective:We propose a new idea that uses knowledge organization structures, such as taxonomies, ontologies and thesauri, asan auxiliary artifact to establish trace links. In order to investigate the novelty and feasibility of this idea, we studytraceability in the area of requirements engineering. Method: First, we conduct a literature survey to investigate towhat extent and how auxiliary artifacts have been used in the past for requirements traceability. Then, we conduct avalidation study in industry, testing the idea of taxonomic trace links with realistic artifacts. Results: We have reviewed126 studies that investigate requirements traceability; ninetey-one of them use auxiliary artifacts in the traceabilityprocess. In the validation study, while we have encountered six challenges when classifying requirements with a domain-specific taxonomy, we found that designers and engineers are able to classify design objects comprehensively and reliably.Conclusions: The idea of taxonomic trace links is novel and feasible in practice. However, the identified challenges needto be addressed to allow for an adoption in practice and enable a transfer to software intensive contexts.

Context and motivation: Classification aids software development activities by organizing requirements in classes for easier access and retrieval. The majority of requirements classification research has, so far, focused on binary or multi-class classification. Question/problem: Multi-label classification with large taxonomies could aid requirements traceability but is prohibitively costly with supervised training. Hence, we investigate zero-short learning to evaluate the feasibility of multi-label requirements classification with large taxonomies. Principal ideas/results: We associated, together with domain experts from the industry, 129 requirements with 769 labels from taxonomies ranging between 250 and 1183 classes. Then, we conducted a controlled experiment to study the impact of the type of classifier, the hierarchy, and the structural characteristics of taxonomies on the classification performance. The results show that: (1) The sentence-based classifier had a significantly higher recall compared to the word-based classifier; however, the precision and F1-score did not improve significantly. (2) The hierarchical classification strategy did not always improve the performance of requirements classification. (3) The total and leaf nodes of the taxonomies have a strong negative correlation with the recall of the hierarchical sentence-based classifier. Contribution: We investigate the problem of multi-label requirements classification with large taxonomies, illustrate a systematic process to create a ground truth involving industry participants, and provide an analysis of different classification pipelines using zero-shot learning. © 2024 IEEE.

Context: In infrastructure projects with design-build contracts, the supplier delivers digital assets (e.g., 2D or 3Dmodels) as a part of the design deliverable. These digital assets should align with the customer requirements. Poorrequirements communication between the customer and the supplier is one of the reasons for project overrun. To thebest of our knowledge, no study have yet investigated challenges in requirements communication in the customer-supplierinterface.Objective: In this article, we investigated the processes of requirements validation, requirements communication, anddigital assets verification, and explored the challenges associated with these processes.Methods: We conducted two exploratory case studies. We interviewed ten experts working with digital assets fromthree companies working on two infrastructure projects (road and railway).Results: We illustrate the activities, stakeholders, and artifacts involved in requirements communication, requirementsvalidation, and digital asset verification. Furthermore, we identified 14 challenges (in four clusters: requirements quality,trace links, common requirements engineering (RE), and project management) and their causes and consequences inthose processes.Conclusion: Communication between the client and supplier in sub-contracted work in infrastructure projects is oftenindirect. This puts pressure on the quality of the tender documents (mainly requirements documents) that provides themeans for communication and controls the design verification processes. Hence, it is crucial to ensure the quality of therequirements documents by implementing quality assurance techniques

We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.

Context. Building autonomous racing car controllers is a growing field of computer science which has been receiving great attention lately. An approach named Artificial Potential Fields (APF) is used widely as a path finding and obstacle avoidance approach in robotics and vehicle motion controlling systems. The use of APF results in a collision free path, it can also be used to achieve other goals such as overtaking and maneuverability. Objectives. The aim of this thesis is to build an autonomous racing car controller that can achieve good performance in terms of speed, time, and damage level. To fulfill our aim we need to achieve optimality in the controller choices because racing requires the highest possible performance. Also, we need to build the controller using algorithms that does not result in high computational overhead. Methods. We used Particle Swarm Optimization (PSO) in combination with APF to achieve optimal car controlling. The Open Racing Car Simulator (TORCS) was used as a testbed for the proposed controller, we have conducted two experiments with different configuration each time to test the performance of our APF- PSO controller. Results. The obtained results showed that using the APF-PSO controller resulted in good performance compared to top performing controllers. Also, the results showed that the use of PSO proved to enhance the performance compared to using APF only. High performance has been proven in the solo driving and in racing competitions, with the exception of an increased level of damage, however, the level of damage was not very high and did not result in a controller shut down. Conclusions. Based on the obtained results we have concluded that the use of PSO with APF results in high performance while taking low computational cost.

Blockchain has become the most revolutionary technology in the 21st century. In recent years, one of the concerns of world energy isn't just sustainability yet, in addition, being secure and reliable also. Since information and energy security are the main concern for the present and future services, this thesis is focused on the challenge of how to trade energy securely on the background of using distributed marketplaces that can be applied. The core technology used in this thesis is distributed ledger, specifically blockchain. Since this technology has recently gained much attention because of its functionalities such as transparency, immutability, irreversibility, security, etc, we tried to convey a solution for the implementation of a secure peer-to-peer (P2P) energy trading network over a suitable blockchain platform. Furthermore, blockchain enables traceability of the origin of data which is called data provenience.

In this work, we applied a secure blockchain technology in peer-to-peer energy sharing or trading system where the prosumer and consumer can trade their energies through a secure channel or network. Furthermore, the service management functionalities such as security, reliability, flexibility, and scalability are achieved through the implementation. \\

This thesis is focused on the current proposals for p2p energy trading using blockchain and how to select a suitable blockchain technique to implement such a p2p energy trading network. In addition, we provide an implementation of such a secure network under blockchain and proper management functions. The choices of the system models, blockchain technology, and the consensus algorithm are based on literature review, and it carried to an experimental implementation where the feasibility of that system model has been validated through the output results. 

Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modelling the normal behaviour in order to identify abnormalities. The choice of model is important, i.e., an unsuitable data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and requirements of the domain problem. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. 

In this thesis, we study and apply a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We focus on three real-world application domains: maritime surveillance, district heating, and online media and sequence datasets. We show the importance of data preprocessing as well as feature selection in building suitable methods for data modelling. We take advantage of both supervised and unsupervised techniques to create hybrid methods. 

More specifically, we propose a rule-based anomaly detection system using open data for the maritime surveillance domain. We exploit sequential pattern mining for identifying contextual and collective outliers in online media data. We propose a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. We develop a few higher order mining approaches for identifying manual changes and deviating behaviours in the heating systems at the building level. The proposed approaches are shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviours. We also investigate the reproducibility of the proposed models in similar application domains.

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge about the data. The proposed approach consists of several different data analysis techniques, such as sequential pattern mining, clustering analysis, consensus clustering and the minimum spanning tree (MST). Initially, a clustering analysis is performed on the extracted patterns to model the behavioural modes of the studied phenomenon for a given time interval. The generated clustering models, which correspond to every two consecutive time intervals, can further be assessed to determine changes in the monitored behaviour. In cases in which significant differences are observed, further analysis is performed by integrating the generated models into a consensus clustering and applying an MST to identify deviating behaviours. The validity and potential of the proposed approach is demonstrated on a real-world dataset originating from a network of district heating (DH) substations. The obtained results show that our approach is capable of detecting deviating and sub-optimal behaviours of DH substations.

In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.

In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE.

In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.

The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

Context. System Development Methodologies (SDM’s) have been an area of intensive research in the field of software engineering. Different software organisations adopt different development methodologies and use different development practices. The frequency of usage of development practices and acceptance factors for adoption of development methodology are crucial for software organisations. The factors of acceptance and development practices differ across geographical locations. Many challenges have been presented in the literature with respect to the mismatch of the development practices across organisations while collaborating across organisations in distributed development. There is no considerable amount of research done in context of differences across development practices and acceptance factors for adoption of a particular development methodology. Objectives. The primary objectives of the research are to find out a) differences in (i) practice usage (ii) acceptance factors such as organisational, social and cultural b) explore the reasons for the differences and also investigate consequences of such differences while collaborating, across organisations located in India and Sweden. Methods. A literature review was conducted by searching in scientific databases for identifying common agile and plan-driven development practices and acceptance theories for development methodologies. Survey was conducted across organisations located in India and Sweden to find out the usage frequency of development practices and acceptance factors. Ten interviews were conducted to investigate, reasons for differences and consequences of differences from the software practitioners from organisations located in India and Sweden. Literature evidences were used to support the results collected from interviews. Results. From the survey, organisations in India have adopted a higher frequency of plan driven practices when compared to Sweden and agile practices were adopted at higher frequency in Sweden when compared to India. The number of organisations adopting "pure agile" methodologies have been significantly higher in Sweden. There was significant differences were found across the acceptance factors such as cultural, organisational, image and career factors between India and Sweden. The factors such as cultural, social, human, business and organisational factors are responsible for such differences across development practices and acceptance factors. Challenges related to communication, coordination and control were found due to the differences, while collaborating between Indian and Sweden sites. Conclusions. The study signifies the importance of identifying the frequency of development practices and also the acceptance factors responsible for adoption of development methodologies in the software organisations. The mismatch between these practices will led to various challenges. The study draws insights into various non-technical factors such as cultural, human, organisational, business and social while collaborating between organisations. Variations across these factors will lead to many coordination, communication and control issues. Keywords: Development Practices, Agile Development, Plan Driven Development, Acceptance Factors, Global Software Development.

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks’ inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes. © 2024 Copyright held by the owner/author(s).

This study is part of a worldwide debate on inclusive innovation systems in developing

countries and particularly on the co-evolutionary processes taking place, seen from the

perspective of a public university. The increasing literature that discusses how innovation

systems and development can foster more inclusive and sustainable societies has

inspired this thesis work. Thus, the main problem handled in the research concerns the

question how socially sensitive research practices and policies at a public university in

Bolivia can be stimulated within emerging innovation system dynamics. In that vein,

empirical knowledge is developed at the Universidad Mayor de San SimoÅLn (UMSS),

Cochabamba as a contribution to experience-based learning in the field. Analysis are

nourished by a dialogue with the work of prominent Latin American scholars and

practitioners around the idea of a developmental university and the democratization

of knowledge. The reader will be able to recognize a recursive transit between theory

and practice, where a number of relevant concepts are contextualized and connected

in order to enable keys of critical interpretation and paths of practices amplification

for social inclusion purposes established. The study shows how, based on a previous

experience, new competences and capacities for the Technology Transfer Unit (UTT)

at UMSS were produced, in this case transforming itself into a University Innovation

Centre. Main lessons gained in that experience came from two pilot cluster development

(food and leather sectors) and a multidisciplinary researchers network (UMSS

Innovation Team) where insights found can improve future collaborative relations between

university and society for inclusive innovation processes within the Bolivian

context.

Software Defined Networking (SDN) modernizes network control, offering streamlined management. However, its centralized structure makes it more vulnerable to distributed Denial of Service (DDoS) attacks, posing serious threats to network stability. This thesis explores the development of a DDoS attack prevention technique in SDN environments using the Ryu controller application. The research aims to address the vulnerabilities in SDN, particularly focusing on flooding and Internet Protocol (IP) spoofing attacks, which are a significant threat to network security. The study employs an experimental approach, utilizing tools like Mininet-VM (VirtualMachine), Oracle VM VirtualBox, and hping3 to simulate a virtual SDN environment and conduct DDoS attack scenarios. Key methodologies include packet sniffing and rule-based detection by integrating Snort IDS (Intrusion Detection System), which is critical for identifying and mitigating such attacks. The experiments demonstrate the effectiveness of the proposed prevention technique, highlighting the importance of proper configuration and integration of network security tools in SDN. This work contributes to enhancing the resilience of SDN architectures against DDoS attacks, offering insights into future developments in network security. 

This paper addresses the problem of protecting cloud environments against targeted attacks, which have become a popular mean of gaining access to organization's confidential information and resources of cloud providers. Only in 2015 eleven targeted attacks have been discovered by Kaspersky Lab. One of them - Duqu2 - successfully attacked the Lab itself. In this context, security researchers show rising concern about protecting corporate networks and cloud infrastructure used by large organizations against such type of attacks. This article describes a possibility to apply a sandboxing method within a cloud environment to enforce security perimeter of the cloud.

This paper addresses the problem of incident response in clouds. A conventional incident response model is formulated to be used as a basement for the cloud incident response model. Minimization of incident handling time is considered as a key criterion of the proposed cloud incident response model that can be done at the expense of embedding infrastructure redundancy into the cloud infrastructure represented by Network and Security Controllers and introducing Security Domain for threat analysis and cloud forensics. These architectural changes are discussed and applied within the cloud incident response model. © 2016 IEEE.

In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens. © 2020 IEEE.

This paper contains an analysis of the payload of the popular ransomware for Windows, Android, Linux, and MacOSX platforms. Namely, VaultCrypt (CrypVault), TeslaCrypt, NanoLocker, Trojan-Ransom.Linux.Cryptor, Android Simplelocker, OSX/KeRanger-A, WannaCry, Petya, NotPetya, Cerber, Spora, Serpent ransomware were put under the microscope. A set of characteristics was proposed to be used for the analysis. The purpose of the analysis is generalization of the collected data that describes behavior and design trends of modern ransomware. The objective is to suggest ransomware threat mitigation techniques based on the obtained information. The novelty of the paper is the analysis methodology based on the chosen set of 13 key characteristics that helps to determine similarities and differences thorough the list of ransomware put under analysis. Most of the ransomware samples presented were manually analyzed by the authors eliminating contradictions in descriptions of ransomware behavior published by different malware research laboratories through verification of the payload of the latest versions of ransomware. © 2017 IEEE.

This paper contains an analysis of the LockerGoga ransomware that was used in the range of targeted cyberattacks in the first half of 2019 against Norsk Hydra-A world top 5 aluminum manufacturer, as well as the US chemical enterprises Hexion, and Momentive-Those companies are only the tip of the iceberg that reported the attack to the public. The ransomware was executed by attackers from inside a corporate network to encrypt the data on enterprise servers and, thus, taking down the information control systems. The intruders asked for a ransom to release a master key and decryption tool that can be used to decrypt the affected files. The purpose of the analysis is to find out tactics and techniques used by the LockerGoga ransomware during the cryptolocker attack as well as an encryption model to answer the question if the encrypted files can be decrypted with or without paying a ransom. The scientific novelty of the paper lies in an analysis methodology that is based on various reverse engineering techniques such as multi-process debugging and using open source code of a cryptographic library to find out a ransomware encryption model. © 2019 IEEE.

Context. Network Function Virtualization was recently proposed by European Telecommunications Standards Institute (ETSI) to improve the network service flexibility by virtualization of network services and applications that run on hardware. To virtualize network functions, the software is decoupled from underlying physical hardware. NFV aims to transform industries by reducing capital investments on hardware by using commercial-of-the-shelf (COTS) hardware. NFV makes rapid innovative growth in telecom services through software based service deployment.

Objectives. This thesis work aims to investigate how business organizations function and the roles in defining a service relationship model. The work also aims to define a service relationship model and to validate it via proof of concept using network function virtualization as a service. For this thesis, we finally apply lean principles for the defined service relationship model to reduce waste and investigate how lean benefits the model to be proven as performance service oriented.

Methods. The essence of this work is to make a business organization lean by investigating its actions and applying lean principles. To elaborate, this thesis work involves in a research of papers from IEEE, TMF, IETF and Ericsson. It results in modelling of a PoC by following requirement analysis methodology and by applying lean principles to eliminate unnecessary processes which doesn’t add any value.

Results. The results of the work include a full-fledged service relationship model that include three service levels with roles that can fit in to requirement specifications of NFV infrastructure. The results also show the service levels functionalities and their relationships between the roles. It has also been observed that the services that are needed to be standardized are defined with syntax for ways to describe network functions. It is observed that lean principles benefit the service relationship model from reducing waste factors and hereby providing a PoC which is performance service oriented.

Conclusions. We conclude that roles defined are fit for the service relationship model designed. Moreover, we conclude that the model can hence contain the flow of service by standardizing the subservices and reducing waste interpreted with lean principles and there is a need for further use case proof of the model in full scale industry trials. It also concludes the ways to describe network functions syntax which follows lean principles that are essential to have them for the sub-services standardization. However, PoC defined can be an assurance to the NFV infrastructure.

In recent years, there has been a significant growth in multimedia services such as mobile video streaming, Video-on-Demand and video conferencing. This has led to the development of various video coding techniques, aiming to deliver high quality video while using available bandwidth efficiently. This upsurge in the usage of video applications has also resulted in making endusers more quality-conscious. In order to meet the users’ expectations, the Quality of Experience (QoE) studies has gained utmost importance from both researchers and service providers. This thesis aims to compare the performance of H.264/AVC, Xvid and WebM/VP8 video codecs in wired and wireless networks. The codec performance is evaluated for different packet loss and delay variation values. The evaluation of codec performance is done using both subjective and objective assessment methods. In subjective assessment method, the evaluation of video codec performance is done using ITU-T recommended Absolute Category Rating (ACR) method. Using this method the perceptual video quality ratings are taken from the users, which are then averaged to obtain Mean Opinion Score. These obtained scores are used to analyze the performance of encoded videos with respect to users’ perception. In addition to subjective assessment method, the quality of encoded video is also measured using objective assessment method. The objective metric SSIM (Structural Similarity) is used to evaluate the performance of encoded videos. Based on the results, it was found that for lower packet loss and delay variation values H.264 showed better results when compared to Xvid and WebM/VP8 whereas, WebM/VP8 outperformed Xvid and H.264 for higher packet loss and delay variation values. On the whole, H.264 and WebM/VP8 performed better than Xvid. It was also found that all three video codecs performed better in wired network when compared to the wireless network.

Email-related incidents/crimes are on the rise owing to the fact that communication by electronic mail (e-mail) has become an important part of our daily lives. The technicality behind e-mail plays an important role when looking for digital evidence that can be used to create a hypothesis that can be used during litigation. During this process, it is needful to have a tool that can help to isolate email incidents as a potential crime scene in the wake of suspected attacks. The problem that this paper is addressing paper, is more centered on realizing an open-source email-forensic tool that used the header analysis approach. One advantage of this approach is that it helps investigators to collect digital evidence from e-mail systems, organize the collected data, analyze and discover any discrepancies in the header fields of an e-mail, and generates an evidence report. The main contribution of this paper focuses on generating a freshly computed hash that is attached to every generated report, to ensure the verifiability, reliability, and integrity of the reports to prove that they have not been modified in any way. Finally, this ensures that the sanctity and forensic soundness of the collected evidence are maintained. © 2023 ACM.

Collaborative artificial intelligence systems (CAISs) aim to work with humans in a shared space to achieve a common goal, but this can pose hazards that could harm human beings. We identify emerging problems in this context and report our vision of and progress toward a risk-driven assurance process for CAISs.

Background: Heterogeneous parallel computing systems utilize the combination of different resources CPUs and GPUs to achieve high performance and, reduced latency and energy consumption. Programming applications that target various processing units requires employing different tools and programming models/languages. Furthermore, selecting the most optimal implementation, which may either target different processing units (i.e. CPU or GPU) or implement the various algorithms, is not trivial for a given context. In this thesis, we investigate the use of machine learning to address the selection problem of various implementation variants for an application running on a heterogeneous system.

Objectives: This study is focused on providing an approach for optimization of heterogeneous parallel computing systems at runtime by building the most efficient machine learning model to predict the optimal implementation variant of an application.

Methods: The six machine learning models KNN, XGBoost, DTC, Random Forest Classifier, LightGBM, and SVM are trained and tested using stratified k-fold on the dataset generated from the matrix multiplication application for square matrix input dimension ranging from 16x16 to 10992x10992.

Results: The results of each machine learning algorithm’s finding are presented through accuracy, confusion matrix, classification report for parameters precision, recall, and F-1 score, and a comparison between the machine learning models in terms of accuracy, run-time training, and run-time prediction are provided to determine the best model.

Conclusions: The XGBoost, DTC, SVM algorithms achieved 100% accuracy. In comparison to the other machine learning models, the DTC is found to be the most suitable due to its low time required for training and prediction in predicting the optimal implementation variant of the heterogeneous system application. Hence the DTC is the best suitable algorithm for the optimization of heterogeneous parallel computing.

In this paper, we analyze the outage probability of multiple-input multiple-output cognitive cooperative radio networks (CCRNs) with multiple opportunistic amplify-and-forward relays. The CCRN applies underlay spectrum access accounting for the interference power constraint of a primary network and utilizes orthogonal space-time block coding to transmit multiple data streams across a number of antennas over several time slots. As such, the system exploits both time and space diversity to improve the transmission reliability over Nakagami.. fading. The CCRN applies opportunistic relaying in which the relay offering the highest signal-to-noise ratio at the receiver is selected to forward the transmit signal. Furthermore, selection combining is adopted at the secondary receiver to process the signal from the direct and relaying transmissions. To evaluate system performance, we derive an expression for the outage probability which is valid for an arbitrary number of antennas at the source, relays, and receiver of the CCRN. Selected numerical results are provided using Mathematica for analysis and Matlab for simulations, to reveal the effect of network parameters on the outage probability of the system.

The exploratory testing (ET) approach is commonly applied in industry, but lacks scientific research. The scientific community needs quantitative results on the performance of ET taken from realistic experimental settings. The objective of this paper is to quantify the effectiveness and efficiency of ET vs. testing with documented test cases (test case based testing, TCT). We performed four controlled experiments where a total of 24 practitioners and 46 students performed manual functional testing using ET and TCT. We measured the number of identified defects in the 90-minute testing sessions, the detection difficulty, severity and types of the detected defects, and the number of false defect reports. The results show that ET found a significantly greater number of defects. ET also found significantly more defects of varying levels of difficulty, types and severity levels. However, the two testing approaches did not differ significantly in terms of the number of false defect reports submitted. We conclude that ET was more efficient than TCT in our experiment. ET was also more effective than TCT when detection difficulty, type of defects and severity levels are considered. The two approaches are comparable when it comes to the number of false defect reports submitted.

Despite the general acceptance that software engineering datasets often contain noisy, irrelevant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal component analysis (PCA); correlation-based feature selection (CFS); consistencybased subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic programming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve—the AUC value averaged over 10-fold cross-validation runs—was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and naïve Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries. © Springer International Publishing Switzerland 2016.

Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

Background. Ransomware is a malicious application encrypting important files on a victim's computer. The ransomware will ask the victim for a ransom to be paid through cryptocurrency. After the system is encrypted there is virtually no way to decrypt the files other than using the encryption key that is bought from the attacker.

Objectives. In this practical experiment, we will examine how machine learning can be used to detect ransomware on a local and network level. The results will be compared to see which one has a better performance.

Methods. Data is collected through malware and goodware databases and then analyzed in a virtual environment to extract system information and network logs. Different machine learning classifiers will be built from the extracted features in order to detect the ransomware. The classifiers will go through a performance evaluation and be compared with each other to find which one has the best performance.

Results. According to the tests, local detection was both more accurate and stable than network detection. The local classifiers had an average accuracy of 96% while the best network classifier had an average accuracy of 89.6%.

Conclusions. In this case the results show that local detection has better performance than network detection. However, this can be because the network features were not specific enough for a network classifier. The network performance could have been better if the ransomware samples consisted of fewer families so better features could have been selected.

This Bachelor Thesis examines how animation and sound design in a design process can illustrate an equal and responsible technological future, based on post-humanist theories and philosophies. During the work we have examined animation methods, participatory design, sound design and technology as well as technological developments. In this thesis, we present the processes and methods we used to create our conformation, which is an animated short film.

During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.

In this research the authors explore a hybrid approach which usesthe basic concept of key frame animation together with proceduralanimation to reduce the number of key frames needed for an animationclip. The two approaches are compared by conducting anexperiment where the participating subjects were asked to ratethem based on their visual appeal.

Since 2019, all public websites in Sweden are legally bound to meet a certain degree of digital accessibility. An additional EU directive is being transposed into national law at the time of publication of this thesis, which will impose corresponding requirements on part of the private sector, such as banking services and e-commerce. This will likely cause increased demand which suppliers of web development and, in turn, their developers must be able to meet. 

The aims of this study are to create an increased awareness of digital accessibility as well as to clarify, from the developer’s perspective, how this degree of accessibility is achieved and what could make application of digital accessibility more efficient. 

In order to achieve this, eight qualitative interviews were conducted, transcribed and thematized in the results section. An inductive thematic analysis has been carried out related to the research questions. It compares the results of previous studies with the outcomes from this study, and shows clear similarities but also differences and new discoveries. 

The study shows that developers have access to evaluation tools and guidelines that provide good support in their work, but that the responsibility often lies with individual developers rather than with the business as a whole. This is one of the main challenges, together with the fact that inaccessible development is still being carried out in parallel, and that time pressure leads to deprioritization of accessibility. However, the respondents agree that it does not take any more time to develop accessible rather than inaccessible websites, provided that this is taken into account from the outset. Success factors for digital accessibility are to sell the idea to the customer, to work in a structured way with knowledge sharing and to document solutions in order to save time. In addition to this, it appears that the implementation of accessibility would benefit from the ownership being raised to a higher decision level and the competence being broadened in the supplier's organization, and that developers gain access to specialist competence and user tests to support their work. A basic knowledge of accessibility could be included in web development training to a greater extent, and an extension of the legal requirements could also create additional incentives for the customer. 

Background: As the demand for cybersecurity professionals continues to rise, it is crucial to identify the key skills necessary to thrive in this field. This research project sheds light on the cybersecurity skills landscape by analyzing the recommendations provided by the European Cybersecurity Skills Framework (ECSF), examining the most required skills in the Swedish job market, and investigating the common skills identified through the findings. The project utilizes the large language model, ChatGPT, to classify common cybersecurity skills and evaluate its accuracy compared to human classification.

Objective: The primary objective of this research is to examine the alignment between the European Cybersecurity Skills Framework (ECSF) and the specific skill demands of the Swedish cybersecurity job market. This study aims to identify common skills and evaluate the effectiveness of a Language Model (ChatGPT) in categorizing jobs based on ECSF profiles. Additionally, it seeks to provide valuable insights for educational institutions and policymakers aiming to enhance workforce development in the cybersecurity sector.

Methods: The research begins with a review of the European Cybersecurity Skills Framework (ECSF) to understand its recommendations and methodology for defining cybersecurity skills as well as delineating the cybersecurity profiles along with their corresponding key cybersecurity skills as outlined by ECSF. Subsequently, a Python-based web crawler, implemented to gather data on cybersecurity job announcements from the Swedish Employment Agency's website. This data is analyzed to identify the most frequently required cybersecurity skills sought by employers in Sweden. The Language Model (ChatGPT) is utilized to classify these positions according to ECSF profiles. Concurrently, two human agents manually categorize jobs to serve as a benchmark for evaluating the accuracy of the Language Model. This allows for a comprehensive assessment of its performance.

Results: The study thoroughly reviews and cites the recommended skills outlined by the ECSF, offering a comprehensive European perspective on key cybersecurity skills (Tables 4 and 5). Additionally, it identifies the most in-demand skills in the Swedish job market, as illustrated in Figure 6. The research reveals the matching between ECSF-prescribed skills in different profiles and those sought after in the Swedish cybersecurity market. The skills of the profiles 'Cybersecurity Implementer' and 'Cybersecurity Architect' emerge as particularly critical, representing over 58% of the market demand. This research further highlights shared skills across various profiles (Table 7).

Conclusion: This study highlights the matching between the European Cybersecurity Skills Framework (ECSF) recommendations and the evolving demands of the Swedish cybersecurity job market. Through a review of ECSF-prescribed skills and a thorough examination of the Swedish job landscape, this research identifies crucial areas of alignment. Significantly, the skills associated with 'Cybersecurity Implementer' and 'Cybersecurity Architect' profiles emerge as central, collectively constituting over 58% of market demand. This emphasizes the urgent need for educational programs to adapt and harmonize with industry requisites. Moreover, the study advances our understanding of the Language Model's effectiveness in job categorization. The findings hold significant implications for workforce development strategies and educational policies within the cybersecurity domain, underscoring the pivotal role of informed skills development in meeting the evolving needs of the cybersecurity workforce.