デジタルデザイン教育における中国の伝統的パターン要素の統合発展に関する研究

Cultural dissemination and artistic heritage represent a highly concentrated database of information in the past, which depends not only on the expansion of emerging digital media but also on the transformation of language and knowledge. Digital design with traditional patterns as elements is a continuous process from creative generation, material selection, and model establishment. The whole process is done based on the digital design course. The author conducts research from three aspects: theoretical knowledge, course operation exercises and after-school questionnaires, which is a process of integrating technology and art. The purpose of this course is not to discover new theories or new technologies, but to provide morphological possibilities for cultural symbols under existing digital technologies. The purpose is to try to construct new ideas and innovative digital technologies for traditional Chinese patterns, to find Possible paths for the inheritance of new traditional culture.北九州市立大

Neolithic and chalcolithic cultures in Turkish Thrace

The subject of this thesis are the Neolithic and Chalcolithic cultures in Turkish Thrace. Turkish Thrace acts as a land bridge between the Balkans and Anatolia. Along this land bridge it might be expected that there has been a transfer of ideas, exchange and movement of objects between two regions. Intensive survey in a selected part of Turkish Thrace - the Edime region - and systematic field collection techniques on selected sites were conducted. Intensive surveys in the Edime region have provided important evidence relating to past land use and settlement systems. On the basis of examination settlements and artefacts, local Neolithic and Chalcolithic cultures closely related to the Balkan cultures were defined. One of the research problems in Turkish Thrace is the apparent dramatic decrease in population in the late Chalcolithic period. All late Chalcolithic sites are small relative to those of other Chalcolithic cultures in the Balkans. There are as yet no geographical studies, soil analysis or pollen diagrams from Turkish Thrace. However, it seems most likely that the depopulation of Turkish Thrace can be explained by a combination of environmental changes, soil changes or exchange network collapse. In Neolithic and Chalcolithic period, some of the Anatolian material looks similar to those of the Balkans. Similarities may be explained by the interaction sphere model. An interaction sphere is defined as an information or item exchange system through which aspects of culture are transferred and which ultimately produces regional similarities. Metabasite stone axes from the Şarköy axe factories were found in the Early Neolithic levels of Hoca Çeşme as well as on settlements in the Edime region. Honey flint of Northeast Bulgaria and Aegean Spondylus were found in the Neolithic and Chalcolithic settlements of Turkish Thrace. These examples begin to introduce the nature of the exchange network in Turkish Thrace

Can There be Art Without an Artist?

Generative Adversarial Network (GAN) based art has proliferated in the past year, going from a shiny new tool to generate fake human faces to a stage where anyone can generate thousands of artistic images with minimal effort. Some of these images are now ``good'' enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artisans --both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners or to traders in the unregulated secondary crypto market. This confluence of factors risks completely detaching humans from the artistic process, devaluing the labor of artists and distorting the public perception of the value of art

Developing the ArchAIDE Application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders

Revisión de los métodos computerizados para la reconstrucción de fragmentos arqueológicos de cerámica

[ES] Las cerámicas son los hallazgos más numerosos encontrados en las excavaciones arqueológicas; a menudo se usan para obtener información sobre la historia, la economía y el arte de un sitio. Los arqueólogos rara vez encuentran jarrones completos; en general, están dañados y en fragmentos, a menudo mezclados con otros grupos de cerámica.El análisis y la reconstrucción de fragmentos se realiza por un operador experto mediante el uso del método manual tradicional. Los artículos revisados proporcionaron evidencias de que el método tradicional no es reproducible, no es repetible, consume mucho tiempo y sus resultados generan grandes incertidumbres. Con el objetivo de superar los límites anteriores, en los últimos años, los investigadores han realizado esfuerzos para desarrollar métodos informáticos que permitan el análisis de fragmentos arqueológicos de cerámica, todo ello destinado a su reconstrucción. Para contribuir a este campo de estudio, en este artículo, se presenta un análisis exhaustivo de las publicaciones disponibles más importantes hasta finales de 2019. Este estudio, centrado únicamente en fragmentos de cerámica, se realiza mediante la recopilación de artículos en inglés de la base de datos Scopus, utilizando las siguientes palabras clave: "métodos informáticos en arqueología", "arqueología 3D", "reconstrucción 3D", "reconocimiento y reconstrucción automática de características", "restauración de reliquias en forma de cerámica ". La lista se completa con referencias adicionales que se encuentran a través de la lectura de documentos seleccionados. Los 53 trabajos seleccionados se dividen en tres períodos de tiempo. Según una revisión detallada de los estudios realizados, los elementos clave de cada método analizado se enumeran en función de las herramientas de adquisición de datos, las características extraídas, los procesos de clasificación y las técnicas de correspondencia. Finalmente, para superar las brechas reales, se proponen algunas recomendaciones para futuras investigaciones.[EN] Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.Highlights:The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.Eslami, D.; Di Angelo, L.; Di Stefano, P.; Pane, C. (2020). Review of computer-based methods for archaeological ceramic sherds reconstruction. Virtual Archaeology Review. 11(23):34-49. https://doi.org/10.4995/var.2020.13134OJS34491123Andrews, S., & Laidlaw, D. H. (2002). Toward a framework for assembling broken pottery vessels. In Proceedings of the National Conference on Artificial Intelligence, (August 2003), (pp. 945-946).Banterle, F., Itkin, B., Dellepiane, M., Wolf, L., Callieri, M., Dershowitz, N., & Scopigno, R. (2017). VASESKETCH: Automatic 3D Representation of Pottery from Paper Catalog Drawings. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1(693548), (pp. 683-690). https://doi.org/10.1109/ICDAR.2017.117Belenguer, C. S., & Vidal, E. V. (2012). Archaeological fragment characterization and 3D reconstruction based on projective GPU depth maps. In Proceedings of the 2012 18th International Conference on Virtual Systems & Multimedia, VSMM 2012: Virtual Systems in the Information Society, (pp. 275-282). https://doi.org/10.1109/VSMM.2012.6365935Blender. (2018). An open-source 3D graphics and animation software. Retrieved from https://www.blender.orgBrown, B. J., Toler-Franklin, C., Nehab, D., Burns, M., Dobkin, D., Vlachopoulos, A., Weyrich, T. (2008). A system for high-volume acquisition and matching of fresco fragments: Reassembling Theran wall paintings. ACM Transactions on Graphics, 27(3). https://doi.org/10.1145/1360612.1360683Cao, Y., & Mumford, D. (2002). Geometric Structure Estimation of Axially Symmetric Pots from Small Fragments. In Proceedings of the signal processing, pattern recognition and applications, IASTED, Crete, Greece, June 25-28, 2002, (pp. 92-97).Cohen, F., Zhang, Z., & Jeppson, P. (2010). Virtual reconstruction of archaeological vessels using convex hulls of surface markings. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, (pp. 55-61). http://dx.doi.org/10.1109/CVPRW.2010.5543528Cohen, F., Zhang, Z., & Liu, Z. (2016). Mending broken vessels a fusion between color markings and anchor points on surface breaks. Multimedia Tools and Applications, 75(7), 3709-3732. https://doi.org/10.1007/s11042-014-2190-0Cooper, D. B., Willis, A., Andrews, S., Baker, J., Cao, Y., Han, D., … others. (2001). Assembling virtual pots from 3D measurements of their fragments. In Proceedings of the 2001 Conference on Virtual Reality, Archeology, and Cultural Heritage, (pp. 241-254). https://doi.org/10.1145/584993.585032Di Angelo, L., Di Stefano, P., Morabito, A. E., & Pane, C. (2018). Measurement of constant radius geometric features in archaeological pottery. Measurement: Journal of the International Measurement Confederation, 124 (March), 138-146. https://doi.org/10.1016/j.measurement.2018.04.016Di Angelo, L., Di Stefano, P., & Pane, C. (2018). An automatic method for pottery fragments analysis. Measurement: Journal of the International Measurement Confederation, 128, 138-148. https://doi.org/10.1016/j.measurement.2018.06.008Di Angelo, Luca, Di Stefano, P., & Pane, C. (2017). Automatic dimensional characterization of pottery. Journal of Cultural Heritage, 26, 118-128. https://doi.org/10.1016/j.culher.2017.02.003Fragkos, S., Tzimtzimis, E., Tzetzis, D., Dodun, O., & Kyratsis, P. (2018). 3D laser scanning and digital restoration of an archaeological find. MATEC Web of Conferences, 178. https://doi.org/10.1051/matecconf/201817803013Funkhouser, T., Shin, H., Toler-Franklin, C., Castañeda, A. G., Brown, B., Dobkin, D., Weyrich, T. (2011). Learning how to match fresco fragments. Journal on Computing and Cultural Heritage, 4(2). https://doi.org/10.1145/2037820.2037824Halir, R., & Menard, C. (1996). Diameter estimation for archaeological pottery using active vision. In Proceedings of the 20th Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) on Pattern Recognition 1996, (pp. 251-261).Halir, R., & Flusser, J. (1997). Estimation of profiles of sherds of archaeological pottery. In Proceedings of the of the Czech Pattern Recognition Workshop (CPRW'97), Czech Republic, February 1997, 1-5, (pp. 126-130).Halir, R. (1999). An Automatic Estimation Of The Axis Of Rotation Of Fragments Of Archaeological Pottery: A Multi-Step Model-Based Approach. In Proceedings of the 7th International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media (WSCG '99) https://semanticscholar.org/0248/ae5a8dca3d2c6bfff282ce481a5625d32362Hall, N. S., & Laflin, S. (1984). A computer aided design technique for pottery profiles. In Computer applications in Archaeology, (pp. 178-188). Computer Center, University of Birmingham Birmingham. Retrieved from https://www.bcin.ca/bcin/detail.app?id=40524Han, D., & Hahn, H. S. (2014). Axis estimation and grouping of rotationally symmetric object segments. Pattern Recognition, 47(1), 296-312. https://doi.org/10.1016/j.patcog.2013.06.022Hlavackova-Schindler, K., Kampel, M., & Sablatnig, R. (2001). Fitting of a Closed Planar Curve Representing a Profile of an Archaeological Fragment. In Proceedings VAST 2001 Virtual Reality, Archeology, and Cultural Heritage, (pp. 263-269). https://doi.org/10.1145/585031.585034Huang, Q. X., Flöry, S., Gelfand, N., Hofer, M., & Pottmann, H. (2006). Reassembling fractured objects by geometric matching. ACM SIGGRAPH 2006 Papers, SIGGRAPH '06, (May), (pp. 569-578). https://doi.org/10.1145/1179352.1141925Igwe, P. C., & Knopf, G. K. (2006). 3D object reconstruction using geometric computing. Geometric Modeling and Imaging New Trends, 9-14. https://doi.org/10.1109/GMAI.2006.1Kalasarinis, I., & Koutsoudis, A. (2019). Assisting pottery restoration procedures with digital technologies. International Journal of Computational Methods in Heritage Science, 3(1), 20-32. https://doi.org/10.4018/ijcmhs.2019010102Kampel, M., & Sablatnig, R. (2003). Profile-based Pottery Reconstruction. In IEEE Proceeding of Conference on Computer Vision and Pattern Recognition Workshops, Wisconsin, June, (pp. 1-6). https://doi.org/10.1109/CVPRW.2003.10007Kampel, M, & Mara, H. (2005). Robust 3D reconstruction of archaeological pottery based on concentric circular rills. In Proceedings of the Sixth International. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'05), Montreux, Switzerland, (pp. 14-20). Retrieved from https://semanticscholar.org/43df/9b3c6fef5aa54964bdc4825a86cc4e9f4531Kampel, M., & Sablatnig, R. (2003). An automated pottery archival and reconstruction system. Journal of Visualization and Computer Animation, 14(3), 111-120. https://doi.org/10.1002/vis.310Kampel, M., & Sablatnig, R. (2004). 3D Puzzling of Archeological Fragments. In Proceedings of 9th Computer Vision Winter Workshop, (February), (pp. 31-40). Retrieved from https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/cvww041Karasik, A., & Smilansky, U. (2011). Computerized morphological classification of ceramics. Journal of Archaeological Science, 38(10), 2644-2657. https://doi.org/10.1016/j.jas.2011.05.023Kashihara, K. (2012). Three-dimensional reconstruction of artifacts based on a hybrid genetic algorithm. In IEEE International Conference on Systems, Man and Cybernetics, (pp. 900-905). https://doi.org/10.1109/ICSMC.2012.6377842Kashihara, K. (2017). An intelligent computer assistance system for artifact restoration based on genetic algorithms with plane image features. International Journal of Computational Intelligence and Applications, 16(3), 1-15. https://doi.org/10.1142/S1469026817500213Kleber, F., & Sablatnig, R. (2009). A survey of techniques for document and archaeology artifact reconstruction. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, (March 2014), (pp. 1061-1065). https://doi.org/10.1109/ICDAR.2009.154Kotoula, E. (2016). Semiautomatic fragments matching and virtual reconstruction: a case study on ceramics. International Journal of Conservation Science, 7(1), 71-86. Retrieved from http://eprints.lincoln.ac.uk/id/eprint/31035/Lucena, M., Martínez-Carrillo, A. L., Fuertes, J. M., Javier Carrascosa Malagón, F., & Ruiz Rodríguez, A. (2016). Decision support system for classifying archaeological pottery profiles based on mathematical morphology. Multimedia Tools and Applications, 75(7), 3677-3691. https://doi.org/10.1007/s11042-014-2063-6Maiza, C., & Gaildrat, V. (2005). Automatic classification of archaeological potsherds. In Proceedings of the 8th International Conference on Computer Graphics and Artificial Intelligence, Limoges, France, May 11-12, 2005, (pp. 135-147). https://semanticscholar.org/3c95/82c3e562b44e7d61dc0fd3487ea3dc977ff3Mara, H., Kampel, M., & Sablatnig, R. (2002). Preprocessing of 3D-Data for Classification of Archaeological Fragments in an Automated System. In Proceedings of the 26th Workshop of the Austrian Association for Pattern Recognition, Vision with Non-Traditional Sensors, (ÖAGM/AAPR), Graz, Austria, 10-11 September 2002, (pp. 257-264). https://doi.org/10.1.1.15.748Mara, H., & Sablatnig, R. (2006). The orientation of fragments of rotationally symmetrical 3D-shapes for archaeological documentation. In Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006, (June), (pp. 1064-1071). https://doi.org/10.1109/3DPVT.2006.105Melero, F. J., Torres, J. C., & Leon, A. (2003). On the interactive 3d reconstruction of Iberian vessels. In 4th International Symposium on Virtual Reality, Archaeology, and Intelligent Cultural Heritage, VAST, 3, (pp. 71-78). http://dx.doi.org/10.2312/VAST/VAST03/071-078Papaioannou, G., Karabassi, E. a., & Theoharis, T. (2000). Automatic Reconstruction of Archaeological Finds-A Graphics Approach. In International Conference on Computer Graphics and Artificial Intelligence, (March), (pp. 117-125). Retrieved from https://semanticscholar.org/6a3c/7ec8f544bbfb83174d868cd406eaaf40f438Papaioannou, G., Karabassi, E. A., & Theoharis, T. (2002). Reconstruction of three-dimensional objects through the matching of their parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 114-124. https://doi.org/10.1109/34.982888Pulli, K. (1999). Multiview registration for large data sets. In Proceedings of Second International Conference on 3D Digital Imaging and Modeling, Ottawa, ON, Canada, 4-8 December 1999, (pp. 160-168). http://doi.org/10.1109/IM.1999.805346Rasheed, N. A., & Nordin, J. (2015a). A Survey of Computer Methods in Reconstruction of 3D Archaeological Pottery Objects. International Journal of Advanced Research, 3(3), 712-714. Retrieved from https://academia.edu.documents/45540231Rasheed, N. A., & Nordin, M. J. (2014). A polynomial function in the automatic reconstruction of fragmented objects. Journal of Computer Science, 10(11), 2339-2348. https://doi.org/10.3844/jcssp.2014.2339.2348Rasheed, N. A., & Nordin, M. J. (2015b). Archaeological fragments classification based on RGB color and texture features. Journal of Theoretical and Applied Information Technology, 76(3), 358-365. Retrieved from http://repository.uobabylon.edu.iq/papers/publication.aspx?pubid=6746Rasheed, N. A., & Nordin, M. J. (2018). Classification and reconstruction algorithms for the archaeological fragments. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.019Rasheed, N. A., Nordin, M. J., Dakheel, A. H., Nados, W. L., & Maaroof, M. K. A. (2017). Classification archaeological fragments into groups. Research Journal of Applied Sciences, Engineering, and Technology, 14(9), 324-333. https://doi.org/10.19026/rjaset.14.5072Sablatnig, R., & Menard, C. (1997). 3D Reconstruction of Archaeological Pottery using Profile Primitives. In Proceedings of I International Workshop on Synthetic-Natural Hybrid Coding and Three-Dimensional Imaging, (pp. 93-96).Sablatnig, R., Menard, C., & Kropatseh, W. (1998). Classification of archaeological fragments using a description language. In Proceedings of European Signal Processing Conference, (Eusipco '98), (pp. 1097-1100), 1998.Sakpere, W. (2019). 3D Reconstruction of Archaeological Pottery from Its Point Cloud. In Proceedings of Iberian Conference on Pattern Recognition and Image Analysis, (pp. 125-136). https://doi.org/10.1007/978-3-030-31332-6_11Shin, H., Doumas, C., Funkhouser, T., Rusinkiewicz, S., Steiglitz, K.,Vlachopoulos, & Weyrich, T. (2010). Analyzing Fracture Patterns in Theran Wall Paintings. In Proceedings of the 11th International Symposium on Virtual Reality, Archaeology - VAST, (pp. 71-78). https://doi.org/10.2312/VAST/VAST10/071-078Son, K., Almeida, E. B., & Cooper, D. B. (2013). Axially symmetric 3D pots configuration system using the axis of symmetry and break curve. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 257-264). https://doi.org/10.1109/CVPR.2013.40Stamatopoulos, M. I., & Anagnostopoulos, C.-N. (2016). 3D digital reassembling of archaeological ceramic pottery fragments based on their thickness profile. The Computing Research Repository (CoRR). Retrieved from https://arxiv.org/abs/1601.05824Toler-Franklin, C., Funkhouser, T., Rusinkiewicz, S., Brown, B., & Weyrich, T. (2010). Multi-Feature Matching of Fresco Fragments. ACM Transactions on Graphics, 29(6), 1-12. https://doi.org/10.1145/1882261.1866207Üçoluk, G., & Hakki Toroslu, I. (1999). Automatic reconstruction of broken 3-D surface objects. Computers and Graphics, 23(4), 573-582. https://doi.org/10.1016/S0097-8493(99)00075-8Vendrell-Vidal, E., & Sánchez-Belenguer, C. (2014). A Discrete Approach for Pairwise Matching of Archaeological Fragments. Journal on Computing and Cultural Heritage, 7(3), 1-19. https://doi.org/10.1145/2597178Willis, A., Orriols, X., & Cooper, D. B. (2003). Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (16-22 June 2003), Madison, Wisconsin, USA (pp. 1-7). https://doi.org/10.1109/CVPRW.2003.10014Willis, A. R., & Cooper, D. B. (2004). Bayesian assembly of 3D axially symmetric shapes from fragments. 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Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links

MINING IN AL-BAHA REGION,SOUTH-WESTERN SAUDI ARABIA IN ISLAMIC-ERA: THE ARCHAEOLOGY OF ASHAM

During the early Islamic empire, gold and silver were necessary for minting coins for the single currency of the Caliphate to facilitate trade from the western Mediterranean to eastern Persia. Iron, copper and tin were also necessary to equip the armies needed to defend and expand the empire, as well as for construction and domestic use. Although people have focused on the Arab-Islamic expansion in terms of military, religious and trade aspects, the internal production of the state has been largely neglected (i.e. mining and making things, the physical basis of the civilization). Thus, studying mining settlements as a model of internal production settlements is necessary to enrich our understanding of mining activity and its role in Islamic civilization and the medieval world. The importance of this study lies in studying the characteristics of the mining landscape in the region of Al-Baha, by analysing the mining activities taking place at the mining settlement of Asham, one of the most famous mining settlements mentioned by several classical Arab writers. These mining sites were surveyed generally and the mining tools examined. After completing the general survey, Asham settlement was surveyed intensively and excavated with six archaeological trenches to study the stratigraphy of the settlement in order to present an overview of the successive occupation levels and to expose more mining evidence. Materials recovered include over 1112 pieces of pounders and grinders, and 2153 fragments of pottery, soapstone and glass. The study of the mining landscape in Al-Baha region indicates that there were at least three metals mined there during the Islamic era: copper, gold and silver. These metals were processed in three patterns of settlements of different compositions and functions with evidence of state supervision and integration with regional and international trade routes. The research critically analysed the classical Arab-Islamic narratives with regard to the archaeological evidence of tools and facilities. The evidence confirms the extensive scale of mining activities in Al-Baha and its importance to the broader Arab-Islamic world. Rudimentary patterns of mining settlement were overhauled with extensive investment and state involvement during the classical Islamic civilization (c. 630-1100 CE), in the context of the great fillip Arabian trade received under the Umayyad and early Abbasid Caliphates which enabled full and stable exploitation of the natural factors amenable to mining in Al-Baha. The decline of the mining settlements was related to the political disintegration of the Abbasid Caliphate and the rise of a series of Turkic dynasties from the Seljuks onwards, reflected in the fact that the natural conditions still facilitate modern mining in the region, which poses a great threat to the important archaeological remains

II Workshop on Late Neolithic Ceramics in Ancient Mesopotamia : pottery in context

Aquest volum és el resultat del workshop celebrat per investigadors i especialistes en ceràmiques del Pròxim Orient que va tenir lloc al MAC-Empúries l'octubre de 2015. Els articles compilats en el llibre han estat escrits per 31 investigadors de 13 nacionalitats diferents i abasten temàtiques diverses al voltant de la producció ceràmica: matèries primeres, tècniques, analítiques, etc. Pel que fa al context geogràfic, els estudis se centren a Turquia, Siria, Irak, Jordània, Israel i Palestina, els països en que va aparèixer la ceràmica per primera vegada en l'àrea mediterrània, i on va experimentar un ràpid procés de transformació morfotipològic i tecnològic

A 3D Digital Approach to the Stylistic and Typo-Technological Study of Small Figurines from Ayia Irini, Cyprus

The thesis aims to develop a 3D digital approach to the stylistic and typo-technological study of coroplastic, focusing on small figurines. The case study to test the method is a sample of terracotta statuettes from an assemblage of approximately 2000 statues and figurines found at the beginning of the 20th century in a rural open-air sanctuary at Ayia Irini (Cyprus) by the archaeologists of the Swedish Cyprus Expedition. The excavators identified continuity of worship at the sanctuary from the Late Cypriot III (circa 1200 BC) to the end of the Cypro-Archaic II period (ca. 475 BC). They attributed the small figurines to the Cypro-Archaic I-II. Although the excavation was one of the first performed through the newly established stratigraphic method, the archaeologists studied the site and its material following a traditional, merely qualitative approach. Theanalysis of the published results identified a classification of the material with no-clear-cut criteria, and their overlap between types highlights ambiguities in creating groups and classes. Similarly, stratigraphic arguments and different opinions among archaeologists highlight the need for revising. Moreover, pastlegislation allowed the excavators to export half of the excavated antiquities, creating a dispersion of the assemblage. Today, the assemblage is still partly exhibited at the Cyprus Museum in Nicosia and in four different museums in Sweden. Such a setting prevents to study, analyse and interpret the assemblageholistically. This research proposes a 3D chaîne opératoire methodology to study the collection’s small terracotta figurines, aiming to understand the context’s function and social role as reflected by the classification obtained with the 3D digital approach. The integration proposed in this research of traditional archaeological studies, and computer-assisted investigation based on quantitative criteria, identified and defined with 3D measurements and analytical investigations, is adopted as a solution to the biases of a solely qualitative approach. The 3D geometric analysis of the figurines focuses on the objects’ shape and components, mode of manufacture, level of expertise, specialisation or skills of the craftsman and production techniques. The analysis leads to the creation of classes of artefacts which allow archaeologists to formulate hypotheses on the production process, identify a common production (e.g., same hand, same workshop) and establish a relative chronological sequence. 3D reconstruction of the excavation’s area contributes to the virtual re-unification of the assemblage for its holistic study, the relative chronological dating of the figurines and the interpretation of their social and ritual purposes. The results obtained from the selected sample prove the efficacy of the proposed 3D approach and support the expansion of the analysis to the whole assemblage, and possibly initiate quantitative and systematic studies on Cypriot coroplastic production