Video super-resolution (VSR) aims to enhance low-resolution videos by leveraging both spatial and temporal information. While deep learning has led to impressive progress, it typically requires centralized data, which raises privacy concerns. Federated learning (FL) offers a privacy-friendly solution, but general FL frameworks often struggle with low-level vision tasks, resulting in blurry, low-quality outputs. To address this, we introduce FedVSR, the first FL framework specifically designed for VSR. It is architecture-agnostic and stateless, and introduces a lightweight loss function based on the Discrete Wavelet Transform (DWT) to better preserve high-frequency details during local training. Additionally, a loss-aware aggregation strategy combines both DWT-based and task-specific losses to guide global updates effectively. Extensive experiments across multiple VSR models and datasets show that FedVSR not only improves perceptual video quality (up to +0.89 dB PSNR, +0.0370 SSIM, -0.0347 LPIPS and 4.98 VMAF) but also achieves these gains with close to zero computation and communication overhead compared to its rivals. These results demonstrate Fed-VSR's potential to bridge the gap between privacy, efficiency, and perceptual quality, setting a new benchmark for federated learning in low-level vision tasks. Please refer to this link for the code. https://github.com/alimd94/FedVSR

Ultra-high-definition (UHD) video datasets have played a critical role in advancing video quality assessment (VQA), compression, and general computer vision (CV) research. Despite recent progress in the availability of 8K datasets, existing resources remain limited in their coverage of real-world urban environments, where complex motion, uncontrolled lighting, crowds, traffic, and nighttime conditions pose challenges that are not adequately represented in cu-rated or semi-controlled recordings. To address this gap, we present VIDEA-Dublin, an additive urban extension to the VIDEA-8K-60FPS dataset, captured entirely in real-world public environments across Dublin, Ireland.

Multimedia streaming has become essential in various applications, such as security, healthcare, and education. However, it is an operation that demands a high amount of resources from CPU, GPU, memory, and network. This creates the need to develop solutions to predict the amount of resources required to provision services to aid in better decision-making for functionalities such as adaptive video quality control and load balancing, thus ensuring the optimal quality of service (QoS) possible to users given any condition. The existing resource utilization prediction solutions in the literature tend to focus on applications related to cloud computing. However, serverbased architectures play a significant role in use cases where data privacy issues require data to remain on-premise such as security camera feeds. Due to the constrained resources within server-based architectures, accurately predicting resource utilization becomes imperative for optimal system performance. In this paper, we present a dataset of the utilization of resources including hardware, network, and power consumption of serverbased architectures in multimedia streaming. The dataset consists of$37^{\prime} 800$different multimedia streaming use cases covering a variety of video resolutions, client numbers, and stream qualities. We detail the process used to collect the dataset and the parameters obtained from the involved systems, analyzing information that can be extracted from the data. Moreover, we establish a benchmark test by evaluating the performance of a plethora of regression models in predicting the resource utilization required from servers for multimedia streaming on a per-resource level pre-service provisioning, where we show that standard regression models can predict the utilization of resources with a root mean squared error of 2.08%, and that power utilization can also be predicted for both CPU and GPU with an error of$<2$Watts. Finally, we evaluate multivariate systems' ability to predict the values of various parameters together, and show that with a deep learning model we can predict resource utilization and power consumption with an accuracy based on the mean absolute error of 94.42% for utilization and$<2$Watts of power consumption error. The data collected on resource utilization can be found on the GitHub repo: https://github.com/talshoura/Resource-Utilization-of-Multimedia

Commit messages provide descriptions of the modifications made in a commit using natural language, making them crucial for software maintenance and evolution. Recent developments in Large Language Models (LLMs) have led to their use in generating high-quality commit messages, such as the Omniscient Message Generator (OMG). This method employs GPT-4 to produce state-of-the-art commit messages. However, the use of proprietary LLMs like GPT-4 in coding tasks raises privacy and sustainability concerns, which may hinder their industrial adoption. Considering that open-source LLMs have achieved competitive performance in developer tasks such as compiler validation, this study investigates whether they can be used to generate commit messages that are comparable with OMG. Our experiments show that an open-source LLM can generate commit messages comparable to those produced by OMG. In addition, through a series of contextual refinements, we propose OMEGA, a commit message generation approach that uses a 4-bit quantized 8B open-source LLM. OMEGA produces state-of-the-art commit messages, surpassing the performance of GPT-4 in practitioners' preference.

In this paper, we introduce DiQP; a novel Transformer-Diffusion model for restoring 8K video quality degraded by codec compression. To the best of our knowledge, our model is the first to consider restoring the artifacts introduced by various codecs (AV1, HEVC) by Denoising Diffusion without considering additional noise. This approach allows us to model the complex, non-Gaussian nature of compression artifacts, effectively learning to reverse the degradation. Our architecture combines the power of Transformers to capture long-range dependencies with an enhanced windowed mechanism that preserves spatiotemporal context within groups of pixels across frames. To further enhance restoration, the model incorporates auxiliary “Look Ahead” and “Look Around” modules, providing both future and surrounding frame information to aid in reconstructing fine details and enhancing overall visual quality. Extensive experiments on different datasets demonstrate that our model outperforms state-of-the-art methods, particularly for high-resolution videos such as 4K and 8K, showcasing its effectiveness in restoring perceptually pleasing videos from highly compressed sources.11https://github.com/alimd94/DiQP.

Pipeline monitoring is crucial for preventing severe environmental and economic losses. Therefore, accurate and timely leak detection is essential. Deep learning has become a vital tool for analyzing time-series data to detect pipeline leaks. A key parameter in this analysis is the window size, which refers to the duration of data segments used for processing within the model. Fixed window sizes often fall short when dealing with dynamic and variable-length sequential data. This research advances a probabilistic search framework called Monte Carlo methods to adapt to the dynamic characteristics of pipeline signals. We systematically optimized window sizes ranging from 3 to 90 seconds using a large volume of industrial pipeline data. Our findings indicate that moderate window sizes, particularly between 45 and 60 seconds, provide an effective balance between reducing misclassified leaks and maintaining high training accuracy. Furthermore, our analysis of resource usage and evaluation times demonstrates that the model's performance is efficient and manageable within the constraints of typical operational environments.

The software engineering industry prioritizes efficiency through the use of tools and processes such as version control and Agile methodologies. Automation has allowed developers to be more productive and deliver superior results. However, automation is not as widespread in software engineering education. To improve educational outcomes and provide more effective feedback to students, this research implements software engineering methods and automation techniques in software engineering education and evaluates their effectiveness. Our focus is on establishing a connection between source code and natural language, allowing us to generate a natural language description from a given source code sample. To generate feedback, we employ deep learning models to learn code representations and gain a deeper understanding of code. However, the complexity of code makes it challenging to learn its representation accurately. After learning about code, we compare students' code with the instructor-provided solution based on configurable thresholds and generate comments to provide guidance. This work extends the Transformer model, GraphCodeBERT, which is a pre-trained model for programming languages that incorporates the inherent structure of code. We utilize both syntax-level information, such as abstract syntax trees, and semantic-level information, such as data flow, during pre-training. The data flow graph has nodes representing variables and edges indicating the “where-the-value-comes-from” relationship between variables. Our model is based on the Transformer neural architecture and uses a gated graph neural network model to learn code embeddings. This function incorporates code structure, a copy mechanism, and relative position representations, allowing the model to better understand the semantics of code. We evaluated our approach on the Java dataset in terms of BLEU, METEOR, and ROUGE-L metrics and compared it with the state-of-the-art code comment generation model, GTrans. Our model demonstrated improvements in two metrics of METEOR and ROUGE-L.

Software engineers often work on multidisciplinary projects as they collaborate with domain experts in a variety of different disciplines to effectively develop software systems. While the focus of software engineering curricula is generally on teaching technical skills, it is highly desirable to provide students with hands-on experience in working on multidisciplinary projects. This research attempts to facilitate the experience of working on projects with stakeholders of diverse expertise through the use of multidisciplinary hackathons. In recent years, hackathons have been considered effective teaching and evaluation tools that can contribute to the development of practical skills for students. Hackathons can be used as an instrument to improve the educational experience through learning by doing in which learners can test their problem-solving, project management, and prioritization skills in a limited time. This research presents a case study of hackathons in the field of health informatics involving students from software engineering and nursing. To facilitate a consistent and supportive multidisciplinary learning experience, the hackathon was introduced as a course component in two different courses in nursing and software engineering. The data presented in this research is from two consecutive executions of the hackathon involving two different cohorts of students in 2022 and 2023. The results of this study show that students believe a multidisciplinary course would be a welcomed addition as it facilitated an exciting collaborative environment to work on real-world problems; providing a unique opportunity for interactions between different disciplines. The addition of hackathons into the courses enhanced the students' understanding of how to work with requirements from gathering to analysis to development, and allowed them the unique opportunity to examine the different challenges, technologies, and tools involved with other disciplines.

Inadequate sleep is a significant factor in chronic conditions such as cardiovascular disease, diabetes, and mental health disorders, and includes not just sleep duration but also the quality of sleep. Although addressing sleep disorders is complex, mitigating their physical and psychological consequences through effective monitoring and control measures is achievable using wearable devices. While there have been a variety of successful works using wearable devices to classify sleep levels, this study investigates the feasibility of machine learning (ML) approaches based on wearable data to predict different levels of sleep by considering physiological parameters including stress. Stress has long been established as one of the most crucial physiological parameters influencing sleep levels. In this paper, we leveraged data collected from wrist-worn devices to classify the levels of sleep using two different datasets: one considering the level of stress and the other excluding it. The objective is to achieve optimal classification performance by employing a diverse set of seven ML-based classifiers, including Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, and XGBoost. By comparing the performances of the models on these two datasets, we observed that the model incorporating stress inputs exhibited superior accuracy in predicting sleep, highlighting the significant role of stress in sleep levels. Moreover, the findings from the models and their comparison indicate that the Random Forest and XGBoost algorithms outperform other ML methods, exhibiting an impressive accuracy of 95 % in accurately predicting different sleep levels.

The interaction between parents and their children is foundational for their social and emotional development and well-being. Among measures that assess parent-child interaction quality, the Parent-Child Interaction Teaching Scale (PCI-TS) is an effective and well-established assessment tool that measures the quality of parent-child interactions. One of the significant hurdles in PCI-TS is identifying parent-child behaviors early on to aid parents in addressing initial behavioral issues. However, manual evaluations are resource-intensive and time-consuming, and restrict the accessibility of such assessments. In this research, we present an ensemble model to categorize various behavior types in parent-child interaction scales such as PCI-TS. We have collected over 2,000 minutes of video of parents and their children emerging in specific tasks such as drawing or object manipulation. These videos have been labeled based on PCI-TS by trained healthcare professionals. Our ensemble model has been trained to recognize emotions and psychological elements within parent-child interactions. This model is adjusted by multiple modalities and aspects of these psychological elements. We posit that our study introduces the first model that employs a new framework to integrate numerous behavioral factors to recognize these psychological elements within parent-child interactions using audio and text modalities. Our suggested model can discern and detect the semantic and syntactic features of audio and language in parent-child interactions according to the PCI-TS scale. The results of our evaluation showed an enhancement in performance when implementing this strategy, compared to similar approaches by 16% in F1-score.

Parent-Child Interaction Therapy (PCIT) is a ther-apeutic approach designed to enhance the interaction between parents and their children. Parent-child interaction quality may be assessed via observations of free-play or structured tasks between parents and young children. Based on these assess-ments, interventionists provide therapy designed to support more optimal parent-child interactions and promote children's developmental outcomes. While conducting these assessments is vital for children's men-tal health development, manual assessments are time-consuming and resource intensive. This work aims to make PCIT accessible by using Artificial Intelligence (AI) to analyze interaction quality, based on linguistic features in an interactive dialogue. We propose a solution to classify the main behavioral classes in the Dyadic Parent-Child Interaction Coding System (DPICS). To the best of our knowledge, our work is the first model that uses a Transformer-based architecture to analyze the emotions and the psychology integral to the parent-child interactions. The proposed model could understand and detect grammatical, syntactic, and emotional characteristics of the language in parent-child interactions. We categorized Natural Language Processing (NLP) strategies in parent-child interaction quality into three categories: deep learning-based, ML-based, and transfer learning-based. The proposed model is followed by a transfer learning strategy that is fine-tuned over a RoBERTa model and considers text in order to produce comparable results without the use of audio. Our results showed that our proposed model can detect behav-ioral aspects of parent-child interaction without the use of further feature engineering or incorporating additional modalities. We achieved a validation accuracy of 90 %, a significant improvement of 11 % compared to the most successful results reported in similar studies, and therapist agreement rates of 80 %.

This paper provides an overview of our open (Software Engineering Practice and Education) SEPE 8K dataset which is made of 40 different 8K (8192 x 4320) video sequences and 40 variant 8K (8192 x 5464) images. The video sequences were captured at a framerate of 29.97 frames per second (FPS) and had been encoded into videos using AVC/H.264, HEVC/H.265, and AV1 codecs at resolutions from 8K to 480p. The images, video sequences, encoded videos, and various other statistics related to the media that make the dataset are stored online, published, and maintained on the repo on GitHub for non-commercial use. In this paper, the dataset components are described and analyzed using various methods. The proposed dataset is - as far as we know - the first to publish true 8K natural sequences; thus, it is important for the next level of applications dealing with multimedia such as video quality assessment, super-resolution, video coding, video compression, and many more.

This Research Full Paper presents quantitative and qualitative data on the effectiveness of hackathons in Software Engineering Education. Hackathons have become a growing part of Software Engineering (SE) education in the past decade. Although the academic environment develops technical foundations, a hackathon can develop key competencies to hone lifelong learning. Improving interpersonal, entrepreneurial, and technical skills better prepare SE students for a career after graduation and reinforces engineering-relevant skills such as problem-solving, teamwork, and management. This study examines students’ abilities to transfer relevant course skills into the hackathon environment, specifically those related to SE best practices such as software design, SOLID principles (Single-responsibility, Open-Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion Principle), and Object-Oriented Programming (OOP). Furthermore, through faculty-led hackathon prep sessions and workshops, students are trained to follow an adapted design-thinking process known as the Hackathon Design Thinking Process (HDTP). This study directly addresses the following sub-questions: to what extent do participants feel that hackathons have a positive impact on their SE education, what are the participants’ perceptions of SOLID principles and OOP skill development during the hackathon, and how effectively are participants able to apple SOLID principles and OOP practices during the hackathon? The data is aggregated from a second-year SE undergraduate and a first-year SE masters cohort from participant perception surveys, judges, and project submissions from two hackathons. We utilize Natural Language Processing (NLP) with Google’s Sentiment Analysis, conduct Kendall’s Tau-b Test using IBM’s SPSS Statistics Tool, and compare Class Diagrams with student-submitted code. Results confirm that participants believe hackathons positively impact their education and tend to sacrifice planning time to implement solutions, often disobeying SE design principles and best practices due to the fast-paced nature of hackathons.

This Research Full Paper presents the implementation of a hybrid Project-Based Learning (PBL) model in a Software Engineering (SE) course to balance the focus on teaching fundamental knowledge and fostering of applied software development skills through a real-world project, accompanied by contextualized learning and Just-In-Time (JIT) teaching to develop students' scalable knowledge of how to intelligently design with respect to SE best practices. The data is collected from 2 semesters spanning over 2019 and 2020. Based on quantitative and qualitative analysis, this study examines the effectiveness of using the hybrid PBL approach in conveying to students the importance of SE best practices such as the SOLID principles which are deemed as timeless. Results support the claim that JIT lectures help students better evaluate their design decisions and ensure they're on the right track for following optimal design patterns and best practices, and that contextualized learning may be used to develop a notion of why design decisions matter outside of the classroom. Although incorporating these pedagogies in hybrid PBL allows for students' conviction of the significance of SE best practices in academic projects, there still exists room to better convey their significance in industry.

Multidisciplinary teams are often a necessity for software projects as they provide the required expertise to effectively solve complex problems. However, efficient collaboration between teams with different disciplines is challenging due to several factors such as considering gaps in knowledge areas, establishing a development process, and understanding different requirements from various groups or stakeholders. The agile methodology, such as scrum, offers a powerful approach to managing the software development process effectively. As part of the agile methodology, some techniques and tools are used to manage requirements change, which is a common practice in multidisciplinary teams. This research aims to leverage process-mining techniques to analyze data from Jira and GitHub to analyze the efficacy of software development process, particularly in multidisciplinary teams. This approach is applied to a case study of a virtual healthcare intervention system to measure the team’s productivity. The results indicate several deficiencies in the process with respect to requirements engineering task that cause loss of time and increase rework rates. Results indicate that there are some challenges in the development process that contribute to some deficiencies. The rework rate is high and the number of tasks that are intended to be completed is less than what was planned. These factors can contribute to the lengthening of the software development process. Most of these challenges can be addressed by improving the requirement engineering process in order to obtain the requirements and manage change requests more efficiently.

Peer-instruction has been shown to be an effective method to support learning. We exploit a form of peer-instruction in an undergraduate course on databases, where students take an exam in teams. Instantly after attempting the exam individually without any immediate feedback, students re-take the exam as a group utilizing immediate feedback instruments. These instruments permit the students to tackle each multiple-choice question several times until a correct answer is uncovered. Our thesis is that this approach provides the students with an opportunity to learn from their mistakes, whether committed individually or as a group, while promoting individual student deep learning. We support this thesis by analyzing data collected from 125 students, 5 group exams, and other assessment instruments, including a final exam.

Within technical engineering courses, students may struggle with difficult concepts, overwhelming workloads, loss of motivation and a lack of classroom engagement. Studies have shown that students who are engaged and creative in their education have improved learning outcomes in technical understanding and application. This work proposes the use of gamification for the development of both creative and technical understanding. Gamification is the application of game mechanics and typical elements of game playing (e.g. point scoring, competition with others, rules of play, etc.) to technical education as a method of encouraging student engagement with course material in a compelling and familiar way. This paper describes the development and implementation of a creative design project within an electronic design automation course, as well as a further teaching and learning research evaluation by general public focus groups.

Although management of Road Safety has been an area of concern over the past several decades the following behavior-based problem areas still exist: unsafe driving behaviors and high-risk drivers. A Behavior-Based Safety Management System (BBSMS) can help address these areas by introducing concepts of Activators, Behaviors and their Consequences. The focus of BBSMS is on improving and changing behavior rather than dealing with the consequences of bad behavior. This paper explores the application of Utilization Z-scores and Reliability Demonstration Chart, a reliability engineering technique, to help analyze driver behavior. The driver behavior is represented by In Vehicle Monitoring System (IVMS) data collected over several years. The events recorded and monitored by the IVMS include over speeding, over revving, harsh acceleration, harsh braking and seat belt disconnects while driving. The techniques provide an easy and effective way for drivers and their managers to monitor driver risk profiles by classifying and identifying drivers with high risks - drivers with a higher probability of generating IVMS events. The consequences of the unsafe behavior can then be identified and activators can be modified in order to reduce risk. By positively influencing the behavior the consequences can be better managed to reduce the risks associated with the Safety Management System (SMS).

Social networks are ever-growing systems by inheritance. The increase in the number nodes in these systems often brings forth the need to add additional functionalities. However due to the distributed nature of social networks, system growth can be a challenging task. Therefore scalability of the system is of vital importance in the design of social networks. This research attempts to establish a comprehensive framework for analysis and validation of requirements and design documents for software systems. In previous work, we applied this framework to analyze the requirements of a social network of agents; expressed using scenario-based specifications. Scenarios are appealing because of their expressive power and simplicity. Moreover due to the clear and concise notation of scenarios, they can be used to analyze the system requirements for general validity, lack of deadlock, and existence of emergent behavior. In this paper a methodology to analyze the scalability of social networks is presented. This methodology is devised to indicate whether or not the new requirements of the system are consistent with the current requirements in place. A larger prototype of a social network of MSA for semantic search is utilized to illustrate the developed methodology.

Road related deaths and injuries continue to be one of the highest incidents recorded in organizations. Road Safety has become a major concern worldwide. Therefore the United Nations has introduced a new movement: UN Decade of Action for Road Safety 2011-2020, which aims to reduce road deaths and injuries worldwide. An effective Safety Management System (SMS) can help in reducing risk of incidents, injuries and fatalities. The National Safety Council defines the three performance areas to benchmark SMS to be Leadership - Management, Technical - Operational and Cultural - Behavioral. This paper proposes a systematic way of finding relationships between Technical - Operational factors, Cultural - Behavioral and Safety Management Systems through the use of Neural networks-fuzzy. A sample SMS is simulated using critical factors (environmental and road conditions). Moreover, neural networks are used to predict the next outcome given historical information of various parameters such as road and weather conditions. Fuzzy logic is used to fuzzily the membership functions. The model helps us understand the effects of factors such as snow, rain and Mean Temperature as well as the events reported by In Vehicle Monitoring System (IVMS) on the number of incidents recorded and the "Road Safety Score". To illustrate the methodology in this paper, the neural networks-fuzzy model is fed with environmental factors to see how they affect the overall number of incidents recorded on a daily basis in the City of Calgary.

Network filtering has become an important security issue worldwide. Network filters are designed and put in place to enforce restrictions for a variety of different motives, such as political, social, economical or merely security reasons. Although network filters can be applied to different networks, their main use is for the Internet. However, as is the case with most network security measures, many network filters are bypassed by users and thus are not completely adequate to perform their tasks. This paper approaches the network filtering concepts from a software engineering perspective. The general purpose of this approach is to utilize automated methodologies to analyze the correctness of the requirements of the filtering mechanisms, and to reduce their vulnerability. In order to achieve this, requirements are expressed using scenario-based specifications. The resulting scenarios are then analyzed for unwanted behavior using automated methodology. To demonstrate the effectiveness of this approach, it is applied to the case study of a real-life Internet-filtering system.

Emergent behavior in distributed systems is a central problem that may lead to unexpected behaviors and major faults. Emergent behaviors are usually categorized into three groups: emergent behaviors occur due to scenarios incompleteness; emergent behaviors as a result of violation of a system wide policy; and emergent behaviors as a result of synthesis of behavior models. In this paper, a technique for addressing the latter group is proposed. The technique prevents from overgeneralization in the behavior model synthesis. Overgeneralization happens as the result of behavior model synthesis and depends on the assumptions of the process. In addition, the proposed technique addresses the issue of the existing ad-hoc methodologies by providing an automated algorithm. This algorithm can be used by a syntax checker to automatically detect and correct the emergent behaviors in the scenarios. The proposed algorithm is validated using a case study of a fleet management system.

Emergent behavior is an important issue in distributed systems' design. Detecting and removing emergent behavior during the design phase will lead to huge savings in deployment costs of such systems. An effective approach for the design of distributed systems is to describe system requirements using scenarios. A scenario, commonly known as a message sequence chart or a sequence diagram, is a temporal sequence of messages sent between system components. However, scenario-based specifications are prone to subtle deficiencies with respect to analysis and validation known as incompleteness and partial description. In this research, a method for detecting emergent behavior of scenario-based specification is proposed. The method is demonstrated and verified using a mine-sweeping robot as an example. Furthermore it has been demonstrated in this paper that scenario-based specifications can be used in agile software development and that the proposed methodologies in this research can be utilized effectively in agile approaches.

Lack of central control makes the design of distributed software systems a challenging task because of possible unwanted behavior at runtime, commonly known as emergent behavior. Developing methodologies to detect emergent behavior prior to the implementation stage of the system can lead to huge savings in time and cost. However manual review of requirements and design documents for real-life systems is inefficient and error prone; thus automation of analysis methodologies is considered greatly beneficial. This paper proposes the utilization of an ontology-based approach to analyze system requirements expressed by a set of message sequence charts (MSC). This methodology involves building a domain-specific ontology of the system, and examines the requirements based on this ontology. The advantages of this approach in comparison with other methodologies are its consistency and increased level of automation. The effectiveness of this approach is explained using a case study of an IntelliDrive system.

Distributed systems are employed in countless applications such as information systems, robotics, etc. Lack of central control makes the design of such systems a challenging task because of possible unwanted behavior at runtime, commonly known as emergent behavior. Developing a methodology to detect emergent behavior in the pre-implementation stages of the software development life-cycle of distributed systems can potentially lead to huge savings in time and cost. Moreover, due to the typical large size of the modern distributed systems, automating the detection methodology is considered greatly beneficial. An effective and efficient approach for the design of distributed systems is to describe system requirements using scenarios. A scenario, commonly known as a message sequence chart (MSC), is a temporal sequence of messages sent between system components. However, scenario-based specifications may contain subtle deficiencies with respect to analysis and validation known as incompleteness and partial description. In this research, a tool to automatically detect emergent behavior of scenario-based specification of distributed systems is developed and demonstrated using a robotics example.