Background: This pilot study has aimed to explore the adoption and effectiveness of mobile gaming applications as nonpharmacological tools for pain management in paediatric oncology within Saudi Arabia.Grounded in a culturally extended Technology Acceptance Model (TAM), the study has incorporated constructs such as Social Influence, Trust, System Quality, and Accessibility to understand technology uptake in a culturally conservative context.Problem: Current research lacks culturally adapted models that account for social, institutional, and technological factors affecting mobile app uptake in paediatric oncology-especially in non-Western contexts like Saudi Arabia.Methods: A mixed-methods pilot study was conducted at the King Fahad National Centre for Children's Cancer Emergency in Riyadh.Quantitative data were collected from 80 participants-50 parents and 30 healthcare providers-using a structured TAM-based survey instrument, with convenience sampling employed for feasibility.Composite Reliability (CR) was calculated using AMOS software as part of Structural Equation Modelling (SEM), with model fit indices such as CFI and RMSEA reported to validate the analytic model.In parallel, qualitative data were gathered from four participants (two parents and two healthcare providers) via semi-structured interviews, analysed thematically using a phenomenological approach.The limited qualitative sample was justified as appropriate for a pilot focused on instrument validation, with limitations due to access, ethics, and time constraints; future work will expand this sampling.The instruments were pre-tested for cultural and linguistic appropriateness through expert review and back-translation.Results: Survey constructs showed strong reliability (α = 0.87-0.93).All six hypotheses were statistically supported, validating the extended TAM framework.Path relationships were tested through SEM, supported by multiple regression, enhancing methodological robustness.Qualitative data prompted question wording changes and highlighted format preferences: parents preferred in-person interviews, providers preferred virtual.Implications: Findings confirm the instrument's reliability and support the extended TAM model.Additionally, these results provide actionable implications for the design and implementation of culturally tailored mHealth interventions.The extended TAM framework can guide developers and healthcare administrators in designing mobile health tools that incorporate elements of social influence and trust into app features, training programs, and communication strategies.These insights support the effective deployment and potential scaling of such technologies within conservative healthcare systems like Saudi Arabia.Moreover, the study explicitly aligned its findings with the four guiding research questions: perceived usefulness and ease of use (RQ1) and social influence (RQ2) significantly predicted adoption; system and information quality (RQ3) enhanced trust; and trust in reliability and data security (RQ4) mediated user acceptance, reinforcing the explanatory strength of the extended TAM in this context.Future studies are recommended to include participant stratification by oncologic treatment phase and implement pre-and post-intervention pain assessments to isolate effects and enhance clinical interpretability.

Saudi Arabia's Vision 2030 prioritises digital transformation (DX) to modernise higher education.However, despite significant investment, Saudi public universities (SPUs) face unique challenges in adoption due to a lack of contextspecific frameworks.To address this gap, this study proposes and validates the novel DXA-SPU framework, an integrated model that combines the Technology Acceptance Model (TAM) and the Technology-Organisation-Environment (TOE) framework.The model was evaluated using survey data from 447 SPU participants, with hypothesised relationships analysed via Structural Equation Modelling (SEM).The results supported 12 of the 14 hypotheses.Perceived usefulness and the institutional skills gap emerged as the most significant drivers of adoption.In turn, DX adoption was strongly linked to enhanced institutional performance, administrative efficiency, technical infrastructure, and teaching effectiveness.The DXA-SPU framework offers a validated tool for university leaders to assess DX readiness and align strategic planning with Vision 2030 goals, providing actionable insights for policymakers.

In recent years, plagiarism detection systems have evolved from basic lexical matching and n-gram overlap methods to Deep Learning (DL) models capable of capturing semantic relationships between texts. While these DL-based approaches have achieved notable success across various languages, their effectiveness in Arabic remains limited due to inherent linguistic ambiguities, particularly the omission of diacritical marks. This absence hinders accurate semantic interpretation and limits the ability of models to detect paraphrased or semantically obfuscated content in Arabic texts. This paper presents an evaluation of Arabic Text Diacritization (ATD) tools as the initial phase of a plagiarism detection framework designed for Arabic–English cross-lingual model text analysis (AraXLM). It describes the first stage of the framework, which focuses on assessing the performance of state-of-the-art ATD tools. An empirical analysis was conducted on six ATD models using Word Error Rate (WER), Diacritic Error Rate (DER), both with and without case endings (CE), and Bilingual Evaluation Understudy (BLEU) metrics. The results show that tools such as Shakkelha produced lower DER and high BLEU values, indicating high accuracy in diacritic restoration, while Fine-Tashkeel demonstrates the lowest WER and highest BLEU, reflecting best word-level performance. In contrast, CAMeL Tools and Mishkal display comparatively higher error rates across both metrics. These findings suggest that incorporating accurate diacritization models into Arabic NLP tasks, such as Machine Translation (MT) and Plagiarism Detection (PD), improves text normalisation and the quality of semantic embeddings. Thus, the AraXLM framework, supported by effective diacritization pre-processing, enhances linguistically aware detection of plagiarism involving Arabic text, where precise semantic alignment between languages is essential.

Studies on Saudi accounting practices have identified evidence of creative accounting in the financial statements of listed companies.Despite the application of various fraud detection methods, identifying legal but misleading manipulations remains challenging.This paper extends the Creative Accounting Detection Model (CADM), an LSTM-based model originally proposed by Bineid et al. (2023, 2024) for detecting creative accounting.Two versions, (CADM1) and (CADM2), were trained on two simulated datasets with different bases, achieving 100% and 95% accuracy, respectively.Testing on the energy sector (2019-2023), CADM1 identified one company as engaging in creative accounting, while CADM2 classified all companies as non-creative with greater confidence stability.The findings establish CADM as a robust, scalable solution for the early detection of financial manipulation.By combining predictive strength with explainability, CADM can be employed to advance current approaches to forensic accounting and risk analytics, offering valuable insights to regulators, auditors, and decision-makers.

Online customer reviews (OCRs) have become vital for shoppers, aiding their purchase decisions amidst the rapid growth of user-generated content.However, limited attention has been paid to studying the impact of OCRs on the purchase intentions of Arab consumers.Therefore, applying Western online review systems to other cultures without further consideration may pose challenges.This study aims to examine how various factors of OCRs affect Arab consumers' buying intentions.Employing a mixed-methods approach, quantitative data from a survey questionnaire (633 responses) and qualitative insights from interviews (15 participants) were collected and analysed sequentially.The findings reveal that review central cues (valence, comprehensiveness, readability and images) and some peripheral cues (volume and reviewer experience) significantly influence purchase intention.By contrast, reviewer identity disclosure and reputation are not deemed important by Arab book shoppers.The semi-structured interviews validated the significance of reading OCRs before purchase, offered insights into the impact of various related factors, and revealed a new factor that is shared perspectives between the reviewer and OCR receiver.The study contributes theoretical insights and provides managerial implications for ORP developers and book publishers, aiming to enhance user experience and drive sales.

Cloud computing technology has emerged as a crucial driver of success for Small and Medium Enterprises (SMEs) globally, accelerating work processes and optimizing operations.Notably, SMEs in developed nations, including the United States and the United Kingdom, have proactively harnessed Cloud computing services, reaping substantial benefits in operational efficiency and time utilization.However, in many developing countries, including Saudi Arabia, most SMEs continue to rely on traditional technology, such as On-Premises Servers, instead of Cloud computing services.To investigate the factors influencing Cloud adoption, a new empirical model, the Adoption of Cloud Computing Model for Saudi Arabian SMEs (ACCM-SME), was developed.This study collected quantitative data from 412 participants representing Saudi SMEs in Riyadh city.The empirical data analysis revealed that 12 out of the 17 tested hypotheses exhibited significant positive influence, while five hypotheses failed to meet the specified research criteria and were consequently rejected.This research underscores the critical need to accelerate Cloud technology adoption among SMEs in developing countries, particularly Saudi Arabia.Bridging this technology gap has the potential to significantly enhance SMEs' competitiveness and operational efficiency, contributing to overall economic development.The ACCM-SME model provides nuanced insights into the factors influencing Cloud adoption, guiding further research.The study's rejected hypotheses highlight areas requiring attention for successful adoption.Policymakers and business leaders can leverage these findings to formulate strategies that facilitate Cloud adoption among SMEs.

Private hospitals aim to provide essential healthcare services while focusing on profit and income growth.They are turning to innovative solutions to enhance medical services efficiency while reducing costs.Cloud computing has arisen as an ideal option, allowing private hospitals to access advanced digital health services without heavy infrastructure investments.Yet, in Saudi private hospitals, the adoption of Cloud computing is remarkably low.Therefore, in this study, we surveyed 650 managers and administrative staff from Saudi private hospitals, using our previously proposed ACC-PH framework to assess factors influencing Cloud computing adoption from technological, organisational, and environmental perspectives.The data were analysed using IBM-SPSS and AMOSvr29.The results revealed the positive influence of 12 out of 13 examined factors.The findings are significant in guiding decision-makers in Saudi private hospitals to establish effective strategies for implementing Cloud computing.These strategies can enable easier adoption of Cloud computing in this essential industry.

After the COVID-19 pandemic, e-learning was adopted by different institutions globally to cope with increasing demands for distance learning, especially in higher education.However, assessing student satisfaction remains challenging due to limitations, such as low motivation without face-to-face interaction.This paper presents a conceptual framework for e-Services Impact Analysis (eSIAF) for higher education institutions in Saudi Arabia.Based on a number of technology acceptance theories, this conceptual framework highlights several models adopted to examine different users' satisfaction with e-learning service quality among students, teachers, administrators, and elearning technologists.This paper is part of ongoing research, which will be followed by data collection from eight higher education institutions.After data collection and further processing, a quantitative method will be used to validate the framework.Based on the findings of the study, different approaches can be adopted to increase the satisfaction level of e-learning in higher educational institutes in Saudi Arabia.

The introduction of mobile banking has revolutionized traditional financial practices, enhancing efficiency, customer experiences, and business models globally.Despite the global advancements in mobile banking, adoption rates remain low in Saudi Arabia.This paper seeks to identify key factors affecting adoption, using a mixed-methods approach.We propose a novel model integrating factors from the DeLone and McLean (D&M) model and the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, complemented by additional factors.Data was gathered through online surveys and customer interviews.Findings revealed that net benefits, compatibility, facilitating conditions, and trust positively influence adoption, while literacy levels and digital skills pose barriers.Our study offers a significant theoretical contribution by synthesizing multiple models and enriches understanding of mobile banking adoption, aiding future research and industry decisions.I.

Saudi healthcare organizations are increasingly using Telemedicine (TM) services to reduce expenses and improve the effectiveness of healthcare delivered.Population aging and the growth of the costs of chronic diseases management has an urgent problem that requires the use of technical solutions that contribute to expanding and improving healthcare services and addressing these issues.Consequently, the growing investments in developing TM products and services have made user acceptance of technology crucial in ensuring effective use.The purpose of this study is to explore the factors influencing Saudi patients and healthcare providers to adopt Internet of Body (IoB) technologies to support diagnosis in TM settings.The Technology Acceptance Model (TAM) is employed in this study as the foundational theoretical framework, extending it with additional constructs to fit the context.The IoB-TMAF model identifies factors influencing the adoption intentions of patients and providers for IoB-based TM system.The influencing factors stem from users' individual contexts (social influence, self-efficacy, attitude, and perceived trust), technological contexts (perceived usefulness, perceived ease of use, task fit, reliability, perceived cost, and perceived privacy control), organizational contexts (facilitating conditions), and health contexts (perceived health risk).This study adds to the existing literature by introducing a comprehensive model to explore the motivational factors driving the effective adoption of IoB-based TM in the Kingdom of Saudi Arabia (KSA).Thus, formulating a strategy for the proper execution aligned with the viewpoints of its users.

The increasing prevalence of diabetes, particularly in Saudi Arabia, calls for effective self-management tools to monitor blood sugar levels, such as Continuous Glucose Monitors.These are medical devices that can be used to track the glucose levels of people without a fingerstick blood sample.However, the adoption of IoT-enabled Continuous Glucose Monitors (IoT-CGM) can be challenging due to the use of new technology.This study proposes the Intention to Adopt IoTenabled Continuous Glucose Monitors (IAI-CGM) a framework, which incorporates practical, technological, and user behaviour considerations based on the Technology Acceptance Model (TAM).The study defines 8 hypotheses that are analysed using structural equation modelling.Data was collected; from 873 type 1 diabetes patients (T1DM) from Saudi Arabia.The model predicts the significant impact of all factors on adoption intent except technology -related self-efficacy (TRSE), enabling the assessment of Saudi T1DM patients for IoT-CGM readiness.Furthermore, the framework's novelty may serve as inspiration for developing comparable frameworks for wearable or attached health monitoring devices in patients with other illnesses and in other geographical locations.

Global financial scandals have demonstrated the harmful impact of creative accounting, a practice where managers creatively manipulate financial reports to conceal a company's actual performance and influence stakeholders' decision-making.Studies showed that Saudi-listed companies use it in preparing financial statements.Despite posing a significant risk to the Saudi financial market, detecting it using ordinary auditing procedures remains challenging.Big data analytics has provided practical applications in auditing, and recently, the employment of Deep Learning in fraud detection has delivered remarkably accurate results.Still, limited research has considered it in detecting creative accounting.This study proposes a novel framework using a hybrid learning approach.It suggests training on a simulated dataset of financial statements prepared (i.e., deliberately manipulated) based on financial statements available in the literature for supervised learning.It is then tested on real-world financial reports from the Saudi Open Data and Saudi Statistics.Our framework contributes to the literature with a new governing approach to limit creative accounting and improve financial reporting quality.

The government of Saudi Arabia has adopted M-Government for the effective delivery of services.One advantage that it offers is unique opportunities for real-time and personalized access to government information and services.However, a low adoption rate of m-Government services by citizens is a common problem in Arab countries, including Saudi Arabia, despite the best efforts of the Saudi government.Therefore, this paper explores the determinants of citizens' intention to adopt and use m-Government services, in order to increase the adoption rate.This study was based on the Mobile Government Adoption and Utilization Model (MGAUM) that was developed for the purpose.Data was collected, and the final sample consisted of 1,286 valid responses.The descriptive analysis presented in this paper indicates that all the proposed factors in our MGAUM model were statistically significant in influencing citizens' intention to adopt and use m-Government services.

Developing a model and validating an instrument for measuring Developing a model and validating an instrument for measuring the adoption the adoption and utilisation of mobile government services and utilisation of mobile government services adoption in Saudi Arabia adoption in Saudi Arabia

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While methods to devise domain-specific or general-purpose mutation operators from real faults exist, they are effort-and error-prone, and do not help the tester to decide whether and how to mutate a given source code element. We propose a novel approach to automatically learn mutants from faults in real programs. First, our approach processes bug fixing changes using fine-grained differencing, code abstraction, and change clustering. Then, it learns mutation models using a deep learning strategy. We have trained and evaluated our technique on a set of ~787k bug fixes mined from GitHub. Our empirical evaluation showed that our models are able to predict mutants that resemble the actual fixed bugs in between 9% and 45% of the cases, and over 98% of the automatically generated mutants are lexically and syntactically correct.

In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients- the code that is reused to craft a patch. We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation. Our approach, DeepRepair, relies on deep learning to reason about code similarities. Code fragments at well-defined levels of granularity in a codebase can be sorted according to their similarity to suspicious elements (i.e., code elements that contain suspicious statements) and statements can be transformed by mapping out-of-scope identifiers to similar identifiers in scope. We examined these new search strategies for patch generation with respect to effectiveness from the viewpoint of a software maintainer. Our comparative experiments were executed on six open-source Java projects including 374 buggy program revisions and consisted of 19,949 trials spanning 2,616 days of computation time. Deep-Repair's search strategy using code similarities generally found compilable ingredients faster than the baseline, jGenProg, but this improvement neither yielded test-adequate patches in fewer attempts (on average) nor found significantly more patches (on average) than the baseline. Although the patch counts were not statistically different, there were notable differences between the nature of DeepRepair patches and jGenProg patches. The results show that our learning-based approach finds patches that cannot be found by existing redundancy-based repair techniques.

Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. We mine millions of bug-fixes from the change histories of GitHub repositories to extract meaningful examples of such bug-fixes. Then, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. Our model is able to fix hundreds of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9% of the cases.

Assessing the similarity between code components plays a pivotal role in a number of Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc. Code similarity is generally measured by relying on manually defined or hand-crafted features, e.g., by analyzing the overlap among identifiers or comparing the Abstract Syntax Trees of two code components. These features represent a best guess at what SE researchers can utilize to exploit and reliably assess code similarity for a given task. Recent work has shown, when using a stream of identifiers to represent the code, that Deep Learning (DL) can effectively replace manual feature engineering for the task of clone detection. However, source code can be represented at different levels of abstraction: identifiers, Abstract Syntax Trees, Control Flow Graphs, and Bytecode. We conjecture that each code representation can provide a different, yet orthogonal view of the same code fragment, thus, enabling a more reliable detection of similarities in code. In this paper, we demonstrate how SE tasks can benefit from a DL-based approach, which can automatically learn code similarities from different representations.

Code clone detection is an important problem for software maintenance and evolution. Many approaches consider either structure or identifiers, but none of the existing detection techniques model both sources of information. These techniques also depend on generic, handcrafted features to represent code fragments. We introduce learning-based detection techniques where everything for representing terms and fragments in source code is mined from the repository. Our code analysis supports a framework, which relies on deep learning, for automatically linking patterns mined at the lexical level with patterns mined at the syntactic level. We evaluated our novel learning-based approach for code clone detection with respect to feasibility from the point of view of software maintainers. We sampled and manually evaluated 398 file- and 480 method-level pairs across eight real-world Java systems; 93% of the file- and method-level samples were evaluated to be true positives. Among the true positives, we found pairs mapping to all four clone types. We compared our approach to a traditional structure-oriented technique and found that our learning-based approach detected clones that were either undetected or suboptimally reported by the prominent tool Deckard. Our results affirm that our learning-based approach is suitable for clone detection and a tenable technique for researchers.

Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields. We propose that software engineering (SE) research is a unique opportunity to use these transformative approaches. Our research examines applications of deep architectures such as recurrent neural networks and stacked restricted Boltzmann machines to SE tasks.

Manually reproducing bugs is time-consuming and tedious. Software maintainers routinely try to reproduce unconfirmed issues using incomplete or no informative bug reports. Consequently, while reproducing an issue, the maintainer must augment the report with information - such as a reliable sequence of descriptive steps to reproduce the bug - to aid developers with diagnosing the issue. This process encumbers issue resolution from the time the bug is entered in the issue tracking system until it is reproduced. This paper presents Crash Droid, an approach for automating the process of reproducing a bug by translating the call stack from a crash report into expressive steps to reproduce the bug and a kernel event trace that can be replayed on-demand. Crash Droid manages trace ability links between scenarios' natural language descriptions, method call traces, and kernel event traces. We evaluated Crash Droid on several open-source Android applications infected with errors. Given call stacks from crash reports, Crash Droid was able to generate expressive steps to reproduce the bugs and automatically replay the crashes. Moreover, users were able to confirm the crashes faster with Crash Droid than manually reproducing the bugs or using a stress-testing tool.

GUI-based models extracted from Android app execution traces, events, or source code can be extremely useful for challenging tasks such as the generation of scenarios or test cases. However, extracting effective models can be an expensive process. Moreover, existing approaches for automatically deriving GUI-based models are not able to generate scenarios that include events which were not observed in execution (nor event) traces. In this paper, we address these and other major challenges in our novel hybrid approach, coined as MONKEYLAB. Our approach is based on the Record→Mine→Generate→Validate framework, which relies on recording app usages that yield execution (event) traces, mining those event traces and generating execution scenarios using statistical language modeling, static and dynamic analyses, and validating the resulting scenarios using an interactive execution of the app on a real device. The framework aims at mining models capable of generating feasible and fully replayable (i.e., Actionable) scenarios reflecting either natural user behavior or uncommon usages (e.g., Corner cases) for a given app. We evaluated MONKEYLAB in a case study involving several medium-to-large open-source Android apps. Our results demonstrate that MONKEYLAB is able to mine GUI-based models that can be used to generate actionable execution scenarios for both natural and unnatural sequences of events on Google Nexus 7 tablets.

Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models. Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts. We show how a particular deep learning model can remember its state to effectively model sequential data, e.g., Streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix. Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects. We experiment with two of the models' hyper parameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization. Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models. Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts. Thus, our work serves as the first step toward deep learning software repositories.

This paper outlines the process of developing and deploying an integrated workflow management solution for our system that uniquely integrates heritage data mashups whose digital content is derived from social network repositories and a specific museum digital collections repository and presentation system called ARCO. This workflow solution accommodates a number of integration techniques, based on social networking with user defined content, and using virtual and augmented reality in a Web 2.0 mashup to dynamically present digital heritage content. Other technologies exploited in this scenario include a web service based Grid solution for generating 3D virtual reconstruction animations. The implementation of the workflow solution is based on the Windows Workflow Foundation while the adoption of the multi-tiered human workflow architecture leads to a fully integrated workflow management engine.

This paper expands on the presentation of a methodology that provides a technology-enhanced exhibition of a cultural artefact through the use of a safe hybrid 2D/3D multimodal interface. Such tangible interactions are based on the integration of a 3DOF orientation tracker and information sensors with a 'Kromstaf' rapid prototype replica to provide tactile feedback. The multimodal interface allows the user to manipulate the object via physical gestures which, during evaluation, establish a profound level of virtual object presence and user satisfaction. If a user cannot manipulate the virtual object effectively many application specific tasks cannot be performed. This paper assesses the usability of the multimodal interface by comparing it with two input devices--the Magellan SpaceMouse, and a 'black box', which contains the same electronics as the multimodal interface but without the tactile feedback offered by the 'Kromstaf' replica. A complete human-centred usability evaluation was conducted utilizing task based measures in the form of memory recall investigations after exposure to the interface in conjunction with perceived presence and user satisfaction assessments. Fifty-four participants across three conditions (Kromstaf, space mouse and black box) took part in the evaluation.

A complete tool chain starting with stereo photogrammetry based digitization of artefacts, their refinement, collection and management with other multimedia data, and visualization using virtual and augmented reality is presented. Our system provides a one-stop-solution for museums to create, manage and present both content and context for virtual exhibitions. Interoperability and standards are also key features of our system allowing both small and large museums to build a bespoke system suited to their needs.

This work proposes a high-level augmented reality interface toolkit that allows the combination of audiovisual information with a real world environment in an easy and interactive way. The system is based on MFC libraries, OpenGL, OpenAL, Microsoft Vision SDK and the vision tracking libraries from the well known ARToolKit. Simple and cost effective hardware complements the software solution. This AR interface toolkit can be used as an exemplar for the development of other applications. Users can interact with the presented information in several different ways. Realistic augmentation is also supported such as soft and hard shadows, without sacrificing the overall efficiency of the system. To illustrate the feasibility of our AR interface toolkit a cultural heritage application for museum environments is briefly presented.

Texture mapping is a fundamental feature of computer graphics image generation. In current PC-based acceleration hardware, MIP ("multum in parvo") mapping with bilinear and trilinear filtering is a commonly used filtering technique for reducing spatial aliasing artifacts. The effectiveness of this technique in reducing image aliasing at the expense of blurring is dependent upon the MIP-map level selection and the associated calculation of screen-space to texture-space pixel scaling. This paper describes an investigation of practical methods for per-pixel and per-primitive level of detail calculation. This investigation was carried out as part of the design work for a screen-space rasterization ASIC. The implementations of several algorithms of comparable visual quality are discussed, and a comparison is provided in terms of per-primitive and per-pixel computational costs.