Refurbishing laptops extends their lives while contributing to reducing electronic waste, which promotes building a sustainable future. To this end, the Danish Technological Institute (DTI) focuses on the research and development of several robotic applications empowered with software, including laptop refurbishing. Cleaning represents a major step in refurbishing and involves identifying and removing stickers from laptop surfaces. Software plays a crucial role in the cleaning process. For instance, the software integrates various object detection models to identify and remove stickers from laptops automatically. However, given the diversity in types of stickers (e.g., shapes, colors, locations), identification of the stickers is highly uncertain, thereby requiring explicit quantification of uncertainty associated with the identified stickers. Such uncertainty quantification can help reduce risks in removing stickers, which, for example, could otherwise result in software faults damaging laptop surfaces. For uncertainty quantification, we adopted the Monte Carlo Dropout method to evaluate six sticker detection models (SDMs) from DTI using three datasets: the original image dataset from DTI and two datasets generated with vision language models, i.e., DALL-E-3 and Stable Diffusion-3. In addition, we presented novel robustness metrics concerning detection accuracy and uncertainty to assess the robustness of the SDMs based on adversarial datasets generated from the three datasets using a dense adversary method. Our evaluation results show that different SDMs perform differently regarding different metrics. Based on the results, we provide SD$M$selection guidelines and lessons learned from various perspectives.

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, namedEpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular,EpiTESTERadopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV,EpiTESTERbenefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors. Next, it calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness ofEpiTESTER, we compare it with a probabilistic search algorithm (Simulated Annealing, SA), a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented), andEpiTESTERwith equal probability for each gene. We evaluateEpiTESTERwith six initial environments from CARLA, an open-source simulator for autonomous driving research, and two end-to-end AV controllers, Interfuser and TCP. Our results show thatEpiTESTERachieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.

With the rapid development of autonomous driving systems (ADSs), testing ADSs under various environmental conditions has become a key method to ensure the successful deployment of ADS in the real world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. Target users include ADS developers who need to validate their systems under various environmental conditions.

The Cancer Registry of Norway (CRN) collects information on cancer patients by receiving cancer messages from different medical entities (e.g., medical labs, hospitals) in Norway. Such messages are validated by an automated cancer registry system: GURI. Its correct operation is crucial since it lays the foundation for cancer research and provides critical cancer-related statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT) for GURI can facilitate various experiments and advanced analyses of the operational state of GURI without requiring intensive interactions with the real system. However, GURI constantly evolves due to novel medical diagnostics and treatment, technological advances, etc. Accordingly, CCDT should evolve as well to synchronize with GURI. A key challenge of achieving such synchronization is that evolving CCDT needs abundant data labelled by the new GURI. To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version. EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer messages from a candidate dataset and query GURI with it. We evaluate EvoCLINICAL on three evolution processes. The precision, recall, and F1 score are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL. Furthermore, we replace the active learning part of EvoCLINICAL with random selection to study the contribution of transfer learning to the overall performance of EvoCLINICAL. Results show that employing active learning in EvoCLINICAL increases its performances consistently.

Autonomous vehicles must operate safely in their dynamic and continuously-changing environment. However, the operating environment of an autonomous vehicle is complicated and full of various types of uncertainties. Additionally, the operating environment has many configurations, including static and dynamic obstacles with which an autonomous vehicle must avoid collisions. Though various approaches targeting environment configuration for autonomous vehicles have shown promising results, their effectiveness in dealing with a continuous-changing environment is limited. Thus, it is essential to learn realistic environment configurations of continuously-changing environment, under which an autonomous vehicle should be tested regarding its ability to avoid collisions. Featured with agents dynamically interacting with the environment, Reinforcement Learning (RL) has shown great potential in dealing with complicated problems requiring adapting to the environment. To this end, we present an RL-based environment configuration learning approach, i.e.,DeepCollision, which intelligently learns environment configurations that lead an autonomous vehicle to crash. DeepCollision employs Deep Q-Learning as the RL solution, and selectscollision probabilityas the safety measure, to construct the reward function. We trained four DeepCollision models and conducted an experiment to compare them with two baselines, i.e., random and greedy. Results show that DeepCollision demonstrated significantly better effectiveness in generating collisions compared with the baselines. We also provide recommendations on configuring DeepCollision with the most suitable time interval based on different road structures.

With the aim to reduce car accidents, autonomous driving attracted a lot of attentions these years. However, recently reported crashes indicate that this goal is far from being achieved. Hence, cost-effective testing of autonomous driving systems (ADSs) has become a prominent research topic. The classical model-based testing (MBT), i.e., generating test cases from test models followed by executing the test cases, is ineffective for testing ADSs, mainly because of the constant exposure to ever-changing operating environments, and uncertain internal behaviors due to employed AI techniques. Thus, MBT must be adaptive to guide test case generation based on test execution results in a step-wise manner. To this end, we propose a natural language and model-based approach, named LiveTCM, to automatically execute and generate test case specifications (TCSs) by interacting with an ADS under test and its environment. LiveTCM is evaluated with an open-source ADS and two test generation strategies: Deep Q-Network (DQN)-based and Random. Results show that LiveTCM with DQN can generate TCSs with 56 steps on average in 60 seconds, leading to 6.4 test oracle violations and covering 14 APIs per TCS on average.