Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI) models such as Large Language Models (LLMs), has become a transformative element in contemporary technology. While these models have unlocked new possibilities, they simultaneously present significant challenges, such as concerns over data privacy and the propensity to generate misleading or fabricated content. Current frameworks for Responsible AI (RAI) often fall short in providing the granular guidance necessary for tangible application, especially for Accountability---a principle that is pivotal for ensuring transparent and auditable decision-making, bolstering public trust, and meeting increasing regulatory expectations. This study bridges the Accountability gap by introducing our effort towards a comprehensive metrics catalogue, formulated through a systematic multivocal literature review (MLR) that integrates findings from both academic and grey literature. Our catalogue delineates process metrics that underpin procedural integrity, resource metrics that provide necessary tools and frameworks, and product metrics that reflect the outputs of AI systems. This tripartite framework is designed to operationalize Accountability in AI, with a special emphasis on addressing the intricacies of GenAI.

It is generally accepted in Software Engineering that code clones – often the result of copy-and-paste of existing code – result in poorer maintainability of software systems. Consequently, a variety of techniques have been devised to detect cloned code in software systems and alert developers of duplicated code. Most techniques operate at the source-code level and require some combination of pretty-printing, tokenization and abstraction in order to improve the comparison of code fragments over purely string-based techniques. Avoiding some of the issues of source-code based approaches, we are investigating the effectiveness of using various similarity measures on Bytecode to identify code clones in Java-based systems in this work. The results of our evaluation on selected Java systems indicate that instruction sequences can be used to effectively detect identical code clones. Especially, we achieved the best performance when using the normalized edit distance among applied similarity measures.

Big data are increasingly collected and stored in a highly distributed infrastructures due to the development of several emerging technologies including sensor network, cloud computing, IoT and mobile computing among many other emerging technologies. In practice, the majority of existing big data processing frameworks (e.g., Hadoop, Spark, Flink) are designed based on the single-cluster setup with the assumptions of centralized management and homogeneous connectivity which makes them sub-optimal and sometimes infeasible to be applied for scenarios that require implementing data analytics jobs on highly distributed data sets (across racks, data centers or multi organizations). We demonstrate HDM-MC, a big data processing framework that is designed to enable the capability of performing large scale data analytics across multi-clusters with minimum extra overhead due to additional scheduling requirements. We describe the architecture and realization of the system using a step-by-step example scenario.