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Paramount computer my companion series provides an understanding and constructive approach towards the learning of the subject. Also provides fundamental knowledge and practices of the computer studies. It is aimed at enhancing IT-related competencies within children and promote application based learning. In this way encourages students to explore different aspects of computers and promotes self-learning.

AIs need a couple of considerations here. First off, while an AI character could easily level up without issue (Optimizing their own programming, for instance) what this would grant them is the abilities of their chosen class, so BAB, saving throw bonuses, special abilities, spellcasting if any, companion characters, all that good stuff. What it would not do is increase HD or HP. I know the separation of HD and BAB is unusual, but there's nothing stopping it from working here.

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The same AI can run around in any of the three bodies. Say we have three AIs, one a mechanic, one a technomancer, and the other a soldier. The soldier, in the mannequin body, can manipulate weaponry and physical objects the same way a human or android might do so. At level 10, he's really quite skilled with his equipment, capable of wielding most guns well, but the body is fragile, and has a ten in every physical stat, which feels terrible for a big meaty fighter type. He doesn't like it because of this. The shield drone is even worse - low overall HD and HP, and the gimmicks it can do don't play to his strenghts; the weaponry on this thing is minimal, the the shield generator is useful but too defensive for his tastes and he has no idea what he's even supposed to do with the hologram emitter. The Rhino class destroyer though, that's his jam. Let the meatbags open doors and interact with computers - he's too busy firing lasers and chainguns and cutting apart anything that comes too close to his allies. With strong HD and good weaponry, he can make use of all his abilities.

Wouldn't this take quite a bit of development time?

It might, if it was being developed in a vacuum. Instead, we already have AI rules in Pathfinder, and confirmation that AIs are going to be front and center in Starfinder; the Mechanic class gets one as a companion, to say nothing for enemies. A PC AI would just have more sense of self than the majority of them and be capable of making their own decisions, having grown beyond their original programing through self-edits or emergent intelligence situations.

What about the Mechanic's robot companion? Couldn't an AI just inhabit that body?

Is there a problem with that? It's a class feature and he'd be actively sacrificing action economy by only having a single body on the field. It's an actively weaker choice than getting his own body and letting the robot companion do its own thing, and in exchange the Mechanic gets more money to spend on things that aren't a body. I'd say that's a decently balanced choice, as anybody who's played high level Pathfinder will tell you that Action Economy is king.

The remainder of this paper is organised as follows. In Section 2, we review related work, including computer vision work in relation to image classification, digital humanities, and explainability, as well as related work on explainability and digital humanities. We also discuss related work on abstract concepts and computer vision. In Section 3, we introduce the ARTstract dataset, including the original data sources, abstract concept selection, image selection, data processing, and dataset integration. In Section 4, we present abstract concept-based image classification baselines, including the experimental setup and the results. In Section 5, we discuss our explainability experiments, including the results from class feature visualizations and their denoising, as well as GradCAM++ feature visualizations. In Section 6, we provide a comprehensive discussion of the results, with a focus on contributions, lessons, and future directions in Section 6. We conclude in Section 8.

To contextualize our study, we examine how the domains of computer vision, explainable AI, and digital humanities intersect and contribute to our understanding of AC image classification and its challenges.

In this section, we succinctly describe two of the most commonly used techniques for post-hoc explainability of CNN-based computer vision models (Vilone & Longo, 2020; Ibrahim & Shafiq, 2023), class activation mapping (CAM) and activation maximisation (AM).

Detecting Abstract Concepts (ACs) from images is a challenging task for computer vision (Hussain et al., 2017) due to subjectivity, class imbalances, and entropy. Specifically, ground-truth generation is highly inconsistent, caused by personal and situational factors such as cultural background, personality, and social context (Zhao et al., 2018). Almost all trainable classes in popular datasets for image classification, such as ImageNetFootnote 9 and Google Open Images,Footnote 10 refer to concrete classes (Abgaz et al., 2021; Ahres & Volk, 2016; Brigato et al., 2022). Moreover, abstract words tend to have higher dispersion ratings due to the wide variety of images returned from a query (Kiela & Bottou, 2014; Lazaridou et al., 2015). Despite these issues, a few works define their task as grappling with abstract concept detection from images (Abgaz et al., 2021; Ye et al., 2019; Kalanat & Kovashka, 2022). For example, Kalanat and Kovashka (2022) builds on previous work (Ye et al., 2019) focusing on classifying advertisement images based on symbol clusters.

The ARTstract dataset uses evoked clusters as a way to label the abstract concepts present in each image. Evoked clusters are groups of abstract concepts that often co-occur together in a given context. The idea of clustering abstract concepts or symbols for their visual evocation was introduced alongside the Ads Dataset (Hussain et al., 2017), and has been used in the little existing research on AC image classification in the context of computer vision, such as Ye and Kovashka (2018) and Kalanat and Kovashka (2022). While the cluster categories are not perfect nor objective, the only available performances for the task of AC image classification use these clusters, which is why we have decided to reuse them to create this dataset. The original clusters were created by analyzing the co-occurrence of abstract concepts in advertisement images. Certain choices that were made by the creators of these clusters are decidedly Western-oriented and may not be shared in all contexts. We advocate for future work to investigate more culturally-sensitive and/or diverse abstract concept datasets.

Abstract concepts stand as high-level semantic units within the realm of computer vision, spotlighting the limitations of binary thinking. Laden with ambiguity, subjectivity, and context-dependency, abstract concepts defy facile classification. The diverse meanings these concepts embody within varying cultural and historical contexts further compound the intricacies of labelling. In stark contrast to problems marked by well-defined categories, such as tame problems, high-level computer vision tasks encapsulate the very essence of wicked problems, demanding collective thought and an integrative approach to navigate their intricate landscape. This conceptualization of wicked versus tame problems draws inspiration from the realm of social planning and political science, where wicked problems, characterized by multidimensionality, multiculturalism, and strong cultural dimensions, necessitate nuanced and collaborative solutions (Rittel, 1967).

The results of this study bring to the fore the wicked nature of the problem of automatically detecting abstract concepts within computer vision. The proposed AC image classification baselines show relatively low performances when compared to other CV tasks on art images, such as style, genre, or artist classification (Tan et al., 2016; Cetinic et al., 2018). The results of Table 2, however, are similar to the results obtained by models that use a similar amount of images on a radically different set of labels compared to ImageNet (Ng et al., 2015). The complexity of detecting abstract concepts might hence stem from the relatively open definition of each abstract concept, which does not explicitly account for their polysemy and association to vastly varied visual data (as seen in the example of danger in Fig. 4). The shallow representation obtained using a CNN-based method is not able to generalise enough to capture the ambiguities of such definitions. The confusion matrices in Fig. 6, indeed, reveal potential co-occurrences of abstract concepts that prompt further investigations, such as in the case of the power class.

We can assume that detecting and correcting bias of computer vision systems in the context of Cultural Heritage (CH) will mostly happen in a post-hoc manner, i.e., after a system has been deployed in real-world situations. This is due to the fact that many models based on similar patterns have already been used in real-world applications, especially in the digital humanities. We believe that the integration of interpretability into CV-based systems in the cultural heritage (CH) field has not received enough attention. This study stands as proof that digital humanities (DH) initiatives can act as valuable arenas for both probing the limits of established CV explanation techniques and pioneering novel methodologies. DH projects, with their interdisciplinary focus and emphasis on interpretation, offer a unique opportunity to combine technical methods with hermeneutic work to develop systems that are interpretable-by-design. We envision our work as one of many DH projects that can contribute to the broader development of more transparent and understandable computer vision systems. With their diagnostic capability, the tools developed in XAI are exciting both to the technical disciplines for improving the systems they develop, and to fields such as Digital Humanities with alternative paths for thinking about the kind of work they do (e.g. by interrogating through explainable methods the way that a system has classified certain cultural objects) (Berry, 2021). 2351a5e196