Armelle Couillet, H. Rougier, D. Todisco, Josserand Marot, Olivier Gillet, I. Crevecoeur
{"title":"New Visual Analytics Tool and Spatial Statistics to Explore Archeological Data: The Case of the Paleolithic Sequence of La Roche-à-Pierrot, Saint-Césaire, France","authors":"Armelle Couillet, H. Rougier, D. Todisco, Josserand Marot, Olivier Gillet, I. Crevecoeur","doi":"10.5334/jcaa.81","DOIUrl":"https://doi.org/10.5334/jcaa.81","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Experimental Improvements to the Volume Ratio and Quantifying Movement Using Stone Artefact Analysis","authors":"Stacey Middleton, Rebecca Phillipps","doi":"10.5334/jcaa.93","DOIUrl":"https://doi.org/10.5334/jcaa.93","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Examining Gender Disparities in Computational Archaeology Publications: A Case Study in the Journal of Computational Applications in Archaeology and the Computer Applications and Quantitative Methods in Archaeology Conference Proceedings","authors":"Phyllis S. Johnson","doi":"10.5334/jcaa.84","DOIUrl":"https://doi.org/10.5334/jcaa.84","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A New Framework for Quantifying Prehistoric Grave Wealth","authors":"Mikkel Nørtoft","doi":"10.5334/jcaa.86","DOIUrl":"https://doi.org/10.5334/jcaa.86","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Depth and Dimension: Exploring the Problems and Potential of Photogrammetric Models for Ancient Coins","authors":"Gala Morris, J. Emmitt, Jeremy Armstrong","doi":"10.5334/jcaa.99","DOIUrl":"https://doi.org/10.5334/jcaa.99","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70675065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Dendrochronological Provenance Patterns. Network Analysis of Tree-Ring Material Reveals Spatial and Economic Relations of Roman Timber in the Continental North-Western Provinces","authors":"R. Visser","doi":"10.5334/jcaa.79","DOIUrl":"https://doi.org/10.5334/jcaa.79","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44563915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classification is through petrographic thin section analysis. However, ceramic petrography is a difficult and often tedious task that requires direct observation and sorting by domain experts. In this paper, a deep learning model is built to automatically recognize and classify ceramic fabrics, which expedites the process of classification and lessens the requirements on experts. The samples consist of images of petrographic thin sections under cross-polarized light originating from the Cocal-period (AD 1000–1525) archaeological site of Guadalupe on the northeast coast of Honduras. Two convolutional neural networks (CNNs), VGG19 and ResNet50, are compared against each other using two approaches to partitioning training, validation, and testing data. The technique employs a standard transfer learning process whereby the bottom layers of the CNNs are pre-trained on the ImageNet dataset and frozen, while a single pooling layer and three dense layers are added to ‘tune’ the model to the thin section dataset. After selecting fabric groups with at least three example sherds each, the technique can classify thin section images into one of five fabric groups with over 93% accuracy in each of four tests. The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections and that it can likely be applied on a larger scale.
{"title":"Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning","authors":"Mike Lyons","doi":"10.5334/jcaa.75","DOIUrl":"https://doi.org/10.5334/jcaa.75","url":null,"abstract":"Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classification is through petrographic thin section analysis. However, ceramic petrography is a difficult and often tedious task that requires direct observation and sorting by domain experts. In this paper, a deep learning model is built to automatically recognize and classify ceramic fabrics, which expedites the process of classification and lessens the requirements on experts. The samples consist of images of petrographic thin sections under cross-polarized light originating from the Cocal-period (AD 1000–1525) archaeological site of Guadalupe on the northeast coast of Honduras. Two convolutional neural networks (CNNs), VGG19 and ResNet50, are compared against each other using two approaches to partitioning training, validation, and testing data. The technique employs a standard transfer learning process whereby the bottom layers of the CNNs are pre-trained on the ImageNet dataset and frozen, while a single pooling layer and three dense layers are added to ‘tune’ the model to the thin section dataset. After selecting fabric groups with at least three example sherds each, the technique can classify thin section images into one of five fabric groups with over 93% accuracy in each of four tests. The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections and that it can likely be applied on a larger scale.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49459373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study uses open-source intelligence to analyse the illicit excavation and illicit trafficking of archaeological goods (and forgeries) across the Balkan-Eastern Mediterranean region(s) of Bosnia and Herzegovina, Croatia, Kosovo, Montenegro, North Macedonia, Serbia and Slovenia. It draws on texts and images that have been published by hundreds of artefact-hunters across tens of online communities and other online platforms. These include online forums; social networks, such as Facebook and Instagram; social media, such as Pinterest and YouTube; generic trading platforms, such as eBay, Etsy and olx.ba; and specialist trading platforms, such as VCoins. It shows how artefact-hunters target sites, features and objects; reveal the objects that are collectible and/or marketable; acquire equipment; form patron-client relationships, peer-to-peer partnerships and other cooperative groups; engage in transnational activity; crowdsource techniques for smuggling; crowdsource ways to avoid being caught or punished; and respond to policing. Often, they give identifying details or leave an electronic paper trail that enables their identification. Such information also reveals the destructiveness of processes of extraction and consumption; the economics of the low-end market in cultural goods from poor countries; the gender dimension in cultural property crime and cyber-enabled crime; and the interaction between political allegiance and criminal activity. Thereby, this study shows how netnography and social network analysis can support intelligence-led policing.
{"title":"It Is Not against the Law, if No-One Can See You: Online Social Organisation of Artefact-Hunting in Former Yugoslavia","authors":"Samuel Andrew Hardy","doi":"10.5334/jcaa.76","DOIUrl":"https://doi.org/10.5334/jcaa.76","url":null,"abstract":"This study uses open-source intelligence to analyse the illicit excavation and illicit trafficking of archaeological goods (and forgeries) across the Balkan-Eastern Mediterranean region(s) of Bosnia and Herzegovina, Croatia, Kosovo, Montenegro, North Macedonia, Serbia and Slovenia. It draws on texts and images that have been published by hundreds of artefact-hunters across tens of online communities and other online platforms. These include online forums; social networks, such as Facebook and Instagram; social media, such as Pinterest and YouTube; generic trading platforms, such as eBay, Etsy and olx.ba; and specialist trading platforms, such as VCoins. It shows how artefact-hunters target sites, features and objects; reveal the objects that are collectible and/or marketable; acquire equipment; form patron-client relationships, peer-to-peer partnerships and other cooperative groups; engage in transnational activity; crowdsource techniques for smuggling; crowdsource ways to avoid being caught or punished; and respond to policing. Often, they give identifying details or leave an electronic paper trail that enables their identification. Such information also reveals the destructiveness of processes of extraction and consumption; the economics of the low-end market in cultural goods from poor countries; the gender dimension in cultural property crime and cyber-enabled crime; and the interaction between political allegiance and criminal activity. Thereby, this study shows how netnography and social network analysis can support intelligence-led policing.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48610972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A methodology for identifying prehistoric local learning communities is proposed. We wish to test possible relationships among communities based on continuity and variability in lithic reduction sequence technological traits with different visibility and malleability. Quantitative features reflecting different technological traits are measured on 3-D models of flint cores in different scales: the ratio between core thickness and reduction surface width, the angle between subsequent bands of production blank scars to the relative striking platform, and the average curvature of the ridge between each blank scar striking platform pair. Continuity and variability in these features are used to establish the relations among lithic assemblages on different hierarchical levels: local learning communities and geographically widespread cultural lineages. The Late Upper Palaeolithic and the Epipalaeolithic of the Southern Levant (ca. 27,000–15,000 cal BP) provide an opportunity to test our method. A progressive increase in territoriality is hypothesized throughout this timespan, yet the precise timing and modes of this phenomenon need to be defined. The present study analyzes six core assemblages attributed to different cultural entities, representing chronologically separated occupations of the Ein Gev area and the coastal Sharon Plain. Continuity in technological traits between the Atlitian (ca. 27,000–26,000 cal BP) and Nizzanan (ca. 20,000–18,500 cal BP) occupations of the Ein Gev area suggests that the same learning community repeatedly settled there during a long time span. Two geographically separate learning communities were defined in the study areas within the Kebaran cultural entity (ca. 24,000–18,000 cal BP); the group occupying the Ein Gev area possibly continued to settle there during the Geometric Kebaran (ca. 18,000–15,000 cal BP). Continuity in more conservative traits of the reduction sequence allows to tie these two communities to the same cultural lineage. The ability to track prehistoric learning communities based on quantitative features helps increase the objectivity and the resolution in the reconstruction of past cultural dynamics.
提出了一种识别史前地方学习社区的方法。我们希望基于具有不同可见性和延展性的岩屑还原序列技术特征的连续性和可变性来测试群落之间的可能关系。在不同尺度的燧石岩心三维模型上测量了反映不同技术特征的定量特征：岩心厚度与压下表面宽度之间的比率、后续生产毛坯疤痕带与相对打击平台之间的角度，以及每个毛坯疤痕打击平台对之间的山脊平均曲率。这些特征的连续性和可变性被用来建立不同等级的石器组合之间的关系：当地的学习社区和地理上广泛分布的文化谱系。旧石器时代晚期和南黎凡特旧石器时代末期（约27000–15000 cal BP）为测试我们的方法提供了机会。假设在整个时间跨度内，领土面积会逐渐增加，但这种现象的确切时间和模式需要确定。本研究分析了属于不同文化实体的六个核心组合，代表了埃因盖夫地区和沿海沙龙平原按时间顺序分离的职业。埃因盖夫地区的Atlitian（约27000–26000 cal BP）和Nizzanan（约20000–18500 cal BP）职业之间技术特征的连续性表明，同一学习群体在很长一段时间内反复定居在那里。在Kebaran文化实体内的研究区域中定义了两个地理上独立的学习社区（约24000–18000 cal BP）；在Geometric Kebaran时期（约18000–15000 cal BP），占领Ein Gev地区的群体可能继续在那里定居。还原序列中更保守的特征的连续性允许将这两个群体与同一文化谱系联系起来。基于数量特征跟踪史前学习社区的能力有助于提高重建过去文化动态的客观性和分辨率。
{"title":"Identifying Local Learning Communities During the Terminal Palaeolithic in the Southern Levant: Multi-scale 3-D Analysis of Flint Cores","authors":"Francesco Valletta, Itamar Dag, L. Grosman","doi":"10.5334/jcaa.74","DOIUrl":"https://doi.org/10.5334/jcaa.74","url":null,"abstract":"A methodology for identifying prehistoric local learning communities is proposed. We wish to test possible relationships among communities based on continuity and variability in lithic reduction sequence technological traits with different visibility and malleability. Quantitative features reflecting different technological traits are measured on 3-D models of flint cores in different scales: the ratio between core thickness and reduction surface width, the angle between subsequent bands of production blank scars to the relative striking platform, and the average curvature of the ridge between each blank scar striking platform pair. Continuity and variability in these features are used to establish the relations among lithic assemblages on different hierarchical levels: local learning communities and geographically widespread cultural lineages. The Late Upper Palaeolithic and the Epipalaeolithic of the Southern Levant (ca. 27,000–15,000 cal BP) provide an opportunity to test our method. A progressive increase in territoriality is hypothesized throughout this timespan, yet the precise timing and modes of this phenomenon need to be defined. The present study analyzes six core assemblages attributed to different cultural entities, representing chronologically separated occupations of the Ein Gev area and the coastal Sharon Plain. Continuity in technological traits between the Atlitian (ca. 27,000–26,000 cal BP) and Nizzanan (ca. 20,000–18,500 cal BP) occupations of the Ein Gev area suggests that the same learning community repeatedly settled there during a long time span. Two geographically separate learning communities were defined in the study areas within the Kebaran cultural entity (ca. 24,000–18,000 cal BP); the group occupying the Ein Gev area possibly continued to settle there during the Geometric Kebaran (ca. 18,000–15,000 cal BP). Continuity in more conservative traits of the reduction sequence allows to tie these two communities to the same cultural lineage. The ability to track prehistoric learning communities based on quantitative features helps increase the objectivity and the resolution in the reconstruction of past cultural dynamics.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47990177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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