Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage

Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks as a social thermometer to study these processes, creating, for this purpose, collections that constitute born-digital archives potentially reusable, searchable, and of interest to other researchers or citizens. However, retrieval and archiving techniques used in social networks within heritage studies are still semi-manual, being a time-consuming task and hindering the reproducibility, evaluation, and open-up of the collections created. By combining Information Retrieval strategies with emerging archival techniques, some of these weaknesses can be left behind. Specifically, pooling is a well-known Information Retrieval method to extract a sample of documents from an entire document set (posts in case of social network’s information), obtaining the most complete and unbiased set of relevant documents on a given topic. Using this approach, researchers could create a reference collection while avoiding annotating the entire corpus of documents or posts retrieved. This is especially useful in social media due to the large number of topics treated by the same user or in the same thread or post. We present a platform for applying pooling strategies combined with expert judgment to create cultural heritage reference collections from social networks in a customisable, reproducible, documented, and shareable way. The platform is validated by building a reference collection from a social network about the recent attacks on patrimonial entities motivated by anti-racist protests. This reference collection and the results obtained from its preliminary study are available for use. This real application has allowed us to validate the platform and the pooling strategies for creating reference collections in heritage studies from social networks.

A Cross-Modal Image and Text Retrieval Method Based on Efficient Feature Extraction and Interactive Learning CAE

In view of the complexity of the multimodal environment and the existing shallow network structure that cannot achieve high-precision image and text retrieval, a cross-modal image and text retrieval method combining efficient feature extraction and interactive learning convolutional autoencoder (CAE) is proposed. First, the residual network convolution kernel is improved by incorporating two-dimensional principal component analysis (2DPCA) to extract image features and extracting text features through long short-term memory (LSTM) and word vectors to efficiently extract graphic features. Then, based on interactive learning CAE, cross-modal retrieval of images and text is realized. Among them, the image and text features are respectively input to the two input terminals of the dual-modal CAE, and the image-text relationship model is obtained through the interactive learning of the middle layer to realize the image-text retrieval. Finally, based on Flickr30K, MSCOCO, and Pascal VOC 2007 datasets, the proposed method is experimentally demonstrated. The results show that the proposed method can complete accurate image retrieval and text retrieval. Moreover, the mean average precision (MAP) has reached more than 0.3, the area of precision-recall rate (PR) curves are better than other comparison methods, and they are applicable.

Measurement of Small-Slope Free-Form Optical Surfaces with the Modified Phase Retrieval

In this paper, we demonstrate the use of the modified phase retrieval as a method for application in the measurement of small-slope free-form optical surfaces. This technique is a solution for the measurement of small-slope free-form optical surfaces, based on the modified phase retrieval algorithm, whose essence is that only two defocused images are needed to estimate the wave front with an accuracy similar to that of the traditional phase retrieval but with less image capturing and computation time. An experimental arrangement used to measure the small-slope free-form optical surfaces using the modified phase retrieval is described. The results of these experiments demonstrate that the modified phase retrieval method can achieve measurements comparable to those of the standard interferometer.

Penyebab Balita tidak Imunisasi Dasar Lengkap di Desa Kalumbatan Kabupaten Banggai Kepulauan

Imunisasi merupakan pencegahan primer yang sangat efektif untuk menghindari terjangkitnya penyakit infeksi. Oleh sebab itu, angka kejadian penyakit infeksi akan menurun, kecacatan serta kematian yang ditimbulkannya pun akan berkurang. Sampai saat ini kasus yang ditemukan dilapangan ternyata masih ada ibu-ibu balita yang tidak mau membawa anaknya untuk diberikan imunisasi. Desa Kalumbatan merupakan satu-satunya Desa di Kecamatan Totikum Selatan yang belum mencapai target Universal Child Immunization (UCI). Penelitian ini bertujuan untuk memperoleh informasi penyebab ibu balita tidak memberikan imunisasi dasar pada balitanya di Desa Kalumbatan Kecamatan Totikum Selatan. Penelitian ini merupakan penelitian kualitatif dengan pendekatan fenomenologi, metode pengumpulan data dengan observasi dan wawancara mendalam (indepht interview). Informan dalam penelitian ini berjumlah 20 orang yakni ibu yang memiliki balita 0-59 bulan yang tidak lengkap imunisasi dasar balitanya, kader Posyandu dan petugas kesehatan yang didapatkan menggunakan metode pengambilan informan dengan teknik sampling menggunakan Snow Ball Sampling. Analisis data melalui tiga prosedur yaitu reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian ini menunjukkan bahwa penyebab ibu balita tidak memberikan imunisasi dasar lengkap pada balitanya di Desa Kalumbatan ditemukan 5 informasi yakni : Takut dan trauma balitanya demam setelah diimunisasi, jarak rumah ke Posyandu yang jauh, sibuk dengan pekerjaan, tidak ada dukungan keluarga dan tidak mengetahui informasi jadwal imunisasi. Perlu adanya atau ditingkatkan promosi kesehatan kepada orang tua maupun peserta posyandu terkait peningkatan pemahaman atau pengetahuan tentang manfaat pentingnya imunisasi. Sehingga mampu meningkatkan cakupan imunisasi dasar di kabupaten Banggai Kepulauan khususnya Desa Kalumbatan Kecamatan Totikum Selatan. Immunization is a very effective primary prevention to avoid contracting infectious diseases. Therefore, the incidence of infectious diseases will decrease, the resulting disability and death will also decrease. Until now, the cases found in the field are still mothers of toddlers who do not want to bring their children to be given immunizations. Kalumbatan Village is the only village in South Totikum District that has not yet reached the Universal Child Immunization (UCI) target. This study aims to obtain information on the reasons why mothers of toddlers do not provide basic immunizations for their toddlers in Kalumbatan Village, South Totikum District. This research is a qualitative research with a phenomenological approach, data collection methods with observation and in-depth interviews (indepht interviews). The informants in this study were 20 people, namely mothers who had toddlers 0-59 months whose basic immunizations were incomplete, Posyandu cadres and health workers who were obtained using the informant retrieval method with a sampling technique using Snow Ball Sampling. Data analysis through three procedures, namely data reduction, data presentation and drawing conclusions. The results of this study indicate that the causes of mothers under five who do not provide complete basic immunization to their toddlers in Kalumbatan Village found 5 information, namely: Fear and trauma of toddlers having fever after being immunized, far from home to Posyandu, busy with work, no family support and not knowing information immunization schedule. It is necessary to have or improve health promotion to parents and posyandu participants related to increasing understanding or knowledge about the benefits of the importance of immunization. So as to increase the coverage of basic immunization in Banggai Islands district, especially Kalumbatan Village, South Totikum District.

Designing a Document Retrieval Method for University Digital Libraries Based on Hadoop Technology

With the development of big data, all walks of life in society have begun to venture into big data to serve their own enterprises and departments. Big data has been embraced by university digital libraries. The most cumbersome work for the management of university libraries is document retrieval. This article uses Hadoop algorithm to extract semantic keywords and then calculates semantic similarity based on the literature retrieval keyword calculation process. The fast-matching method is used to determine the weight of each keyword, so as to ensure an efficient and accurate document retrieval in digital libraries, thus completing the design of the document retrieval method for university digital libraries based on Hadoop technology.

Retrieval of Daily Mean VIIRS SST Products in China Seas

Sea surface temperature (SST) is one of the most important factors in regulating air-sea heat flux and, thus, climate change. Most of current global daily SST products are derived from one or two transient measurements of polar-orbiting satellites, which are not the same to daily mean SST values. In this study, high-temporal-resolution SST measurements (32–40 snapshots per day) from a geostationary satellite, FengYun-4A (FY–4A), are used to analyze the diurnal variation of SST in China seas. The results present a sinusoidal pattern of the diurnal variability in SST, with the maximum value at 13:00–15:00 CST and the minimum at 06:00–08:00 CST. Based on the diurnal variation of SST, a retrieval method for daily mean SST products from polar-orbiting satellites is established and applied to 7716 visible infrared imaging radiometer (VIIRS) data in China seas. The results suggest that it is feasible and practical for the retrieval of daily mean SST with an average RMSE of 0.133 °C. This retrieval method can also be utilized to other polar-orbiting satellites and obtain more daily mean satellite SST products, which will contribute to more accurate estimation and prediction between atmosphere and ocean in the future.

An Improved Method Combining CNN and 1D-Var for the Retrieval of Atmospheric Humidity Profiles from FY-4A/GIIRS Hyperspectral Data

FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.