This paper [1] presents an innovative approach to address the prevalent challenge of unbalanced datasets in coin type recognition, shifting the focus from coin class type recognition to coin mint recognition. Despite this shift, the issue of unbalanced data persists. To mitigate this, the authors introduce a method to split larger classes into smaller ones, integrating them into an 'additional class layer'.

Three distinct machine learning (ML) methodologies were employed to identify new possible classes, with one approach utilising unsupervised clustering alongside manual intervention, while the others leverage object detection, and Natural Language Processing (NLP) techniques. However, despite these efforts, overfitting remained a persistent issue, prompting the authors to explore alternative methods such as dataset improvement and Generative Adversarial Networks (GANs).

The paper contributes significantly to the intersection of ML techniques and archaeology, particularly in addressing overfitting challenges. Furthermore, the authors' candid acknowledgment of the limitations of their approaches serves as a valuable resource for researchers encountering similar obstacles.

This study stems from the D4N4 project, aimed at developing a machine learning-based coin recognition model for the extensive "Corpus Nummorum" dataset, comprising over 19,600 coin types and 49,000 coins from various ancient landscapes. Despite encountering challenges with overfitting due to the dataset's imbalance, the authors' exploration of multiple methodologies and transparent documentation of their limitations enriches the academic discourse and provides a foundation for future research in this field.

Reference

[1] Gampe, S. and Tolle, K. (2024). Creating an Additional Class Layer with Machine Learning to counter Overfitting in an Unbalanced Ancient Coin Dataset. Zenodo, 8298077, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8298077

Managing data in archaeology is a perennial problem. As the adage goes, every day in the field equates to several days in the lab (and beyond). For better or worse, past archaeologists did all their organizing and synthesis manually, by hand, but since the 1970s ways of digitizing data for long term management and analysis have gained increasing attention [1]. It is debatable whether this ever actually made things easier, particularly given the associated problem of sustainable maintenance and accessibility of the data. Many older archaeologists, for instance, still have reels and tapes full of data that now require a new form of archaeology to excavate (see [2] for an unrealized idea on how to solve this).

Today, the options for managing digital archaeological data are limited only by one’s imagination. There are systems built specifically for archaeology, such as Arches [3], Ark [4], Codifi [5], Heurist [6], InTerris Registries [7], OpenAtlas [8], S-Archeo [9], and Wild Note [10], as well as those geared towards museum collections like PastPerfect [11] and CatalogIt [12], among others. There are also mainstream databases that can be adapted to archaeological needs like MS Access [13] and Claris FileMaker [14], as well as various web database apps that function in much the same way (e.g., Caspio [15], dbBee [16], Amazon's Simpledb [17], Sci-Note [18], etc.) — all with their own limitations in size, price, and utility. One could also write the code for specific database needs using pre-built frameworks like those in Ruby-On-Rails [19] or similar languages. And of course, recent advances in machine-learning and AI will undoubtedly bring new solutions in the near future.

But let’s be honest — most archaeologists probably just use Excel. That's partly because, given all the options, it is hard to decide the best tool and whether its worth changing from your current system, especially given few real-world examples in the literature. Bastien Rueff’s new paper [20] is therefore a welcomed presentation on the use of Omeka S [21] to manage data collected for the Timbers in Minoan and Mycenaean Architecture (TiMMA) project. Omeka S is an open-source web-database that is based in PHP and MySQL, and although it was built with the goal of connecting digital cultural heritage collections with other resources online, it has been rarely used in archaeology. Part of the issue is that Omeka Classic was built for use on individual sites, but this has now been scaled-up in Omeka S to accommodate a plurality of sites. 

Some of the strengths of Omeka S include its open-source availability (accessible regardless of budget), the way it links data stored elsewhere on the web (keeping the database itself lean), its ability to import data from common file types, and its multi-lingual support. The latter feature was particularly important to the TiMAA project because it allowed members of the team (ranging from English, Greek, French, and Italian, among others) to enter data into the system in whatever language they felt most comfortable.

However, there are several limitations specific to Omeka S that will limit widespread adoption. Among these, Omeka S apparently lacks the ability to export metadata, auto-fill forms, produce summations or reports, or provide basic statistical analysis. Its internal search capabilities also appear extremely limited. And that is not to mention the barriers typical of any new software, such as onerous technical training, questionable long-term sustainability, or the need for the initial digitization and formatting of data. But given the rather restricted use-case for Omeka S, it appears that this is not a comprehensive tool but one merely for data entry and storage that requires complementary software to carry out common tasks.

As such, Rueff has provided a review of a program that most archaeologists will likely not want or need. But if one was considering adopting Omeka S for a project, then this paper offers critical information for how to go about that. It is a thorough overview of the software package and offers an excellent example of its use in archaeological practice.

NOTES

[1] Doran, J. E., and F. R. Hodson (1975) Mathematics and Computers in Archaeology. Harvard University Press.

[2] Snow, Dean R., Mark Gahegan, C. Lee Giles, Kenneth G. Hirth, George R. Milner, Prasenjit Mitra, and James Z. Wang (2006) Cybertools and Archaeology. Science 311(5763):958–959.

[3] https://www.archesproject.org/

[4] https://ark.lparchaeology.com/

[5] https://codifi.com/

[6] https://heuristnetwork.org/

[7] https://www.interrisreg.org/

[8] https://openatlas.eu/

[9] https://www.skinsoft-lab.com/software/archaelogy-collection-management

[10] https://wildnoteapp.com/

[11] https://museumsoftware.com/

[12] https://www.catalogit.app/

[13] https://www.microsoft.com/en-us/microsoft-365/access

[14] https://www.claris.com/filemaker/

[15] https://www.caspio.com/

[16] https://www.dbbee.com/

[17] https://aws.amazon.com/simpledb/

[18] https://www.scinote.net/

[19] https://rubyonrails.org/

[20] Rueff, Bastien (2023) Dealing with Post-Excavation Data: The Omeka S TiMMA Web-Database. peer-reviewed and recommended by Peer Community in Archaeology. https://zenodo.org/records/7989905

[21] https://omeka.org/

This paper (Sosic et al. 2024) explores the Neolithic landscape of the Sopot culture in Đakovština, Eastern Slavonija, revealing a network of settlements through a multi-faceted approach that combines aerial archaeology, magnetometry, excavation, and field survey. This strategy facilitates scalable research tailored to the particularities of each site and allows for improved representations of buried archaeology with minimal intrusion. 

Using the site of Tomašanci-Dubrava as an example of the overall approach, the study further explores the use of drone imagery for 3D surface modeling, revealing a consistent correlation between crop surface elevation during full plant growth and ground terrain after ploughing, attributed to subsurface archaeological features. Results are correlated with magnetic survey and test-pitting data to validate the micro-topography and clarify the relationship between different subsurface structures.

The results obtained are presented in a comprehensive way, including their source data, and are contextualized in relation to conventional cropmark detection approaches and expectations. I found this aspect very interesting, since the crop surface and terrain models contradict typical or textbook examples of cropmark detection, where the vegetation is projected to appear higher in ditches and lower in areas with buried archaeology (Renfrew & Bahn 2016, 82). Regardless, the findings suggest the potential for broader applications of crop surface or canopy height modelling in landscape wide surveys, utilizing ALS data or aerial photographs.  

It seems then that the authors make a valid argument for a layered approach in landscape-based site detection, where aerial imagery can be used to accurately map the topography of areas of interest, which can then be further examined at site scale using more demanding methods, such as geophysical survey and excavation. This scalability enhances the research's relevance in broader archaeological and geographical contexts and renders it a useful example in site detection and landscape-scale mapping.

References

Renfrew, C. and Bahn, P. (2016). Archaeology: theories, methods and practice. Thames and Hudson. 

Sosic Klindzic, R., Vuković, M., Kalafatić, H. and Šiljeg, B. (2024). Digital surface models of crops used in archaeological feature detection – a case study of Late Neolithic site Tomašanci-Dubrava in Eastern Croatia, Zenodo, 7970703, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7970703

The paper “Exploiting RFID Technology and Robotics in the Museum” (Dimitriou et al 2023) is a relevant contribution to museology and an interface between the public, archaeological discourse and the field of social robotics. It deals well with these themes and is concise in its approach, with a strong visual component that helps the reader to understand what is at stake. 

The option of demonstrating the different steps that lead to the final construction of the robot is appropriate, so that it is understood that it really is a linked process and not simple tasks that have no connection. The use of RFID technology for topological movement of social robots has been continuously developed (e.g., Corrales and Salichs 2009; Turcu and Turcu 2012; Sequeira and Gameiro 2017) and shown to have advantages for these environments. Especially in the context of a museum, with all the necessary precautions to avoid breaching the public's privacy, RFID labels are a viable, low-cost solution, as the authors point out (Dimitriou et al 2023), and, above all, one that does not require the identification of users. It is in itself part of an ambitious project, since the robot performs several functions and not just one, a development compared to other currents within social robotics (see Hellou et al 2022: 1770 for a description of the tasks given to robots in museums). The robotic system itself also makes effective use of the localization system, both physically, by RFID labels and by knowing how to situate itself with the public visiting the museum, adapting to their needs, which is essential for it to be successful (see Gasteiger, Hellou and Ahn 2022: 690 for the theme of localization). Archaeology can provide a threshold of approaches when it comes to social robotics and this project demonstrates that, bringing together elements of interaction, education and mobility in a single method. Hence, this is a paper with great merit and deserves to be recommended as it allows us to think of the museum as a space where humans and non-humans can converge to create intelligible discourses, whether in the historical, archaeological or cultural spheres.

References

Dimitriou, A. G., Papadopoulou, S., Dermenoudi, M., Moneda, A., Drakaki, V., Malama, A., Filotheou, A., Raptopoulos Chatzistefanou, A., Tzitzis, A., Megalou, S., Siachalou, S., Bletsas, A., Yioultsis, T., Velentza, A. M., Pliasa, S., Fachantidis, N., Tsagkaraki, E., Karolidis, D., Tsoungaris, C., Balafa, P. and Koukouvou, A. (2024). Exploiting RFID Technology and Robotics in the Museum. Zenodo, 7805387, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology.  https://doi.org/10.5281/zenodo.7805387

Corrales, A. and Salichs, M.A. (2009). Integration of a RFID System in a Social Robot. In: Kim, JH., et al. Progress in Robotics. FIRA 2009. Communications in Computer and Information Science, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03986-7_8

Gasteiger, N., Hellou, M. and Ahn, H.S. (2023). Factors for Personalization and Localization to Optimize Human–Robot Interaction: A Literature Review. Int J of Soc Robotics 15, 689–701. https://doi.org/10.1007/s12369-021-00811-8

Hellou, M., Lim, J., Gasteiger, N., Jang, M. and Ahn, H. (2022). Technical Methods for Social Robots in Museum Settings: An Overview of the Literature. Int J of Soc Robotics 14, 1767–1786 (2022). https://doi.org/10.1007/s12369-022-00904-y

Sequeira, J. S., and Gameiro, D. (2017). A Probabilistic Approach to RFID-Based Localization for Human-Robot Interaction in Social Robotics. Electronics, 6(2), 32. MDPI AG. http://dx.doi.org/10.3390/electronics6020032

Turcu, C. and Turcu, C. (2012). The Social Internet of Things and the RFID-based robots. In: IV International Congress on Ultra Modern Telecommunications and Control Systems, St. Petersburg, Russia, 2012, pp. 77-83. https://doi.org/10.1109/ICUMT.2012.6459769

The archaeological record comes in many forms. Some, such as buried sites from volcanic eruptions or other abrupt sedimentary phenomena are perhaps the only ones that leave relatively clean snapshots of moments in the past. And even in those cases time is compressed. Much, if not all other archaeological record is a messy affair. Things, whatever those things may be, artifacts or construction works (i.e., features), moved, modified, destroyed, warped and in a myriad of ways modified from their behavioral contexts. Do we at some point say the record is worthless? Not worth the effort or continuing investigation. Perhaps sometimes this may be justified, but as Daffara and colleagues show, heavily impacted archaeological remains can give us clues and important information about the past. Thoughtful and careful prehistorians can make significant contributions from what appear to be poor archaeological records. 

            In the case of Daffara and colleagues, a number of important theoretical cross-sections can be recognized. For a long time surface archaeology was thought of simply as a way of getting a preliminary peak at the subsurface. From some of the earliest professional archaeologists (e.g., Kidder 1924, 1931; Nelson 1916) to the New Archaeologists of the 1960s, the link between the surface and subsurface was only improved in precision and systematization (Binford et al. 1970). However, at Hatchery West Binford and colleagues not only showed that surface material can be used more reliably to get at the subsurface, but that substantive behavioral inferences can be made with the archaeological record visible on the surface.

            Much more important are the behavioral implications drawn from surface material. I am not sure we can cite the first attempts at interpreting prehistory from the surface manifestations of the archaeological record, but a flurry of such approaches proliferated in the 1970s and beyond (Dunnell and Dancey 1983; Ebert 1992; Foley 1981).  Off-site archaeology, non-site archaeology, later morphing into landscape archaeology all deal strictly with surface archaeological record to aid in understanding the past. With the current paper, Daffara and colleagues (2024) are clearly in this camp. Although still not widely accepted, it is clear that some behaviors (parts of systems) can only be approached from surface archaeological record. It is very unlikely that a future archaeologist will be able to excavate an entire human social/cultural system; people moving from season to season, creating multiple long and short term camps, travelling, procuring resources, etc. To excavate an entire system one would need to excavate 20,000 km2 or some similarly impossible task. Even if it was physically possible to excavate such an enormous area, it is very likely that some of contextual elements of any such system will be surface manifestations. 

Without belaboring the point, surface archaeological record yields data like any other archaeological record. We must contextual the archaeological artifacts or features weather they come from surface or below. Daffara and colleagues show us that we can learn about deep prehistory of northern Italy, with collections that were unsystematically collected, biased by agricultural as well as other land deformations agents. They carefully describe the regional prehistory as we know it, in particular specific well documented sites and assemblages as a means of applying such knowledge to less well controlled or uncontrolled collections.

References

Binford, L., Binford, R. S. R., Whallon, R. and Hardin, M. A. (1970). Archaeology of Hatchery West. Memoirs of the Society for American Archaeology, No. 24, Washington D.C.

Daffara, S., Giraudi, C., Berruti, G. L. F., Caracausi, S. and Garanzini, F. (2024). First evidence of a Palaeolithic frequentation of the Po plain in Piedmont: the case of Trino (north-western Italy), OSF Preprints, pz4uf, ver. 6 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.31219/osf.io/pz4uf

Dunnell, R. C. and Dancey, W. S. (1983). The siteless survey: a regional scale data collection strategy. In Advances in Archaeological Method and Theory, vol. 6, edited by Michael B. Schiffer, pp. 267-287. Academic Press, New York.

Ebert, J. I. (1992). Distributional Archaeology. University of New Mexico Press, Albuquerque.

Foley, R. A. (1981). Off site archaeology and human adaptation in eastern Africa: An analysis of regional artefact density in the Amboseli, Southern Kenya. British Archaeological Reports International Series 97. Cambridge Monographs in African Archaeology 3. Oxford England.

Kidder, A. V. (1924). An Introduction to the Study of Southwestern Archaeology, With a Preliminary Account of the Excavations at Pecos. Papers of the Southwestern Expedition, Phillips Academy, no. 1. New Haven, Connecticut.

Kidder, A. V. (1931). The Pottery of Pecos, vol. 1. Papers of the Southwestern Expedition, Phillips Academy. New Haven, Connecticut.

Nelson, N. (1916). Chronology of the Tano Ruins, New Mexico. American Anthropologist 18(2):159-180.