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Author(s):  
Justin Tonra ◽  
David Kelly

Eververse was a yearlong conceptual poetry project which used a poet’s biometric data as the basis for generating verse. This article describes the project’s conceptual contributions to the field of electronic literature and its technical development. Eververse operated by collecting biometric data from the poet with a commercial fitness tracking device; this data was sent to a custom-built poetry generator which deployed a number of processes from the domains of Natural Language Generation and Sentiment Analysis to generate poetry; the form and content of this poetry was designed to vary according to specific changes in the biometric data, resulting in a poetry that conspicuously correlated with the poet’s daily activities; this poetry was published in real-time on the project website and the full poem and associated data have now been archived. In addition to providing details on the technical implementation of Eververse, this article includes discussion that situates the work within the tradition of electronic literature and analyses its unique inscription of biometric data. The article examines that feature in the contemporary context of the quantified self, but also in its engagement with historic poetic theories of composition, creativity, and the textualisation of the body.


Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2432
Author(s):  
Nabil Abdoun ◽  
Safwan El Assad ◽  
Thang Manh Hoang ◽  
Olivier Deforges ◽  
Rima Assaf ◽  
...  

In this paper, we propose, implement and analyze an Authenticated Encryption with Associated Data Scheme (AEADS) based on the Modified Duplex Construction (MDC) that contains a chaotic compression function (CCF) based on our chaotic neural network revised (CNNR). Unlike the standard duplex construction (SDC), in the MDC there are two phases: the initialization phase and the duplexing phase, each contain a CNNR formed by a neural network with single layer, and followed by a set of non-linear functions. The MDC is implemented with two variants of width, i.e., 512 and 1024 bits. We tested our proposed scheme against the different cryptanalytic attacks. In fact, we evaluated the key and the message sensitivity, the collision resistance analysis and the diffusion effect. Additionally, we tested our proposed AEADS using the different statistical tests such as NIST, Histogram, chi-square, entropy, and correlation analysis. The experimental results obtained on the security performance of the proposed AEADS system are notable and the proposed system can then be used to protect data and authenticate their sources.


2021 ◽  
pp. 175-204
Author(s):  
Sara Erasmus ◽  
◽  
Saskia van Ruth ◽  

Food fraud is an ongoing global challenge that is amplified by the complexity of supply chain networks and fraudsters becoming more innovative in the way they commit fraud. There is a great need for rapid analytical tools that offer broad product screening. Non-targeted methods provide an approach by which a food matrix can be analysed and screened for adulterations. While various developments exist for rapid non-targeted approaches, there are still multiple challenges to overcome. More work is required to validate, harmonise and standardise non-targeted methods and the associated data interpretations. Promising advances include novel technological developments with devices becoming smaller and portable with increased sensitivity. It is undoubtedly that fingerprinting approaches generate huge datasets that need to be stored and utilised as effectively as possible; creating new opportunities for Big data analysis and the Internet of Things – both addressing the need to convert data into insights to act upon.


2021 ◽  
Vol 24 (68) ◽  
pp. 89-103
Author(s):  
João Batista Pacheco Junior ◽  
Henrique Mariano Costa do Amaral

The design and manual insertion of new terrestrial roads into geographic databases is a frequent activity in geoprocessing and their demand usually occurs as the most up-to-date satellite imagery of the territory is acquired. Continually, new urban and rural occupations emerge, for which specific vector geometries need to be designed to characterize the cartographic inputs and accommodate the relevant associated data. Therefore, it is convenient to develop a computational tool that, with the help of artificial intelligence, automates what is possible in this respect, since manual editing depends on the limits of user agility, and does it in images that are usually easy and free to access. To test the feasibility of this proposal, a database of RGB images containing asphalted urban roads is presented to the K-Means++ algorithm and the SegNet Convolutional Neural Network, and the performance of each was evaluated and compared for accuracy and IoU of road identification. Under the conditions of the experiment, K-Means++ achieved poor and unviable results for use in a real-life application involving tarmac detection in RGB satellite images, with average accuracy ranging from 41.67% to 64.19% and average IoU of 12.30% to 16.16%, depending on the preprocessing strategy used. On the other hand, the SegNet Convolutional Neural Network proved to be appropriate for precision applications not sensitive to discontinuities, achieving an average accuracy of 87.12% and an average IoU of 71.93%.


EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Tao Liu

Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.


2021 ◽  
Author(s):  
Valerio Sbragaglia ◽  
Lucía Espasandín Soneira ◽  
Salvatore Coco ◽  
Alberto Felici ◽  
Ricardo A. Correia ◽  
...  

Fisheries are among the human activities that are most strongly affected by ongoing climate-related changes in the presence and abundance of fish species across the globe. The ecological and social repercussions of such changes for recreational fisheries are however still poorly understood. Here, we explore selected ecological and social dimensions of recreational angling and spearfishing targeting the bluefish (Pomatomus saltatrix) in Italy. The bluefish has undergone a northward expansion in the region over the last 20-30 years, during which it reached new areas and increased in abundance. Using digital videos and their associated data published by recreational fishers on YouTube we characterized ecological and social dimensions using a culturomics approach. Specifically, we focused on harvesting patterns, social engagement and sentiments related to the bluefish. Our study revealed four major results: (i) similar harvesting patterns (i.e., declared mass and seasonal upload patterns) related to videos by recreational anglers and spearfishers; (ii) higher social engagement (i.e., number of views and likes) for videos by recreational anglers than spearfishers; (iii) differences in themes of discussion, with anglers being mainly interested in fishing strategy and gears and spearfishers being more interested in fishing actions shown on the videos; iv) positive and negative sentiments of both recreational anglers and spearfishers towards the invasiveness and aggressiveness of the species. The latter represents an interesting trade-off associated with recreational fishing of the bluefish: it is perceived as an invasive species, but it is also a valued fish target because its voracity contributes to the quality of the recreational fishing experience. Our study showcases the value of exploring social media and associated data to better understand the ecological and human dimensions of marine recreational fisheries in relation to distributional range shifts of species associated with climate change.


Author(s):  
Sara Hansen ◽  
Jutta Buschbom ◽  
Talia Karim ◽  
Anna Monfils

The Extended Specimen was first described by Webster (2017). He defined a “constellation of specimen preparations and data types,” centered around an occurrence of an organism, which captures the breadth of empirical facts about an organism’s phenotype, genotype, and ecology in space and time. The Extended Specimen Network was embraced by the collections community in the Biodiversity Collections Network Extended Specimen Report (Lendemer et al. 2020) and the National Academies of Science, Engineering, and Medicine Future of Collections report (Lendemer et al. 2020, National Academies of Science, Engineering, and Medicine 2020). Several global discussions are underway to build a common definition of the Digital Extended Specimen (DES) and elucidate next steps in building the infrastructure to support Digital Extended Specimens and their network of associated data (including efforts among Distributed System of Scientific Collections (DiSSCo), Biodiversity Collections Network (BCoN), GBIF’s Alliance for Biodiversity Knowledge, TDWG's Task Group on Minimum Information about a Digital Specimen (MIDS), and others.) At the foundation of the DES is the occurrence of an organism in time and space, which is represented by physical specimens or observations serving as tokens of reality. Tokens are translated to digital records, which can be extended through a network of linkages between them and with derived and associated data, e.g. project methodologies, environmental conditions, habitat characteristics, and associated taxa. For digital records to be integrated with the larger network of Digital Extended Specimens, they must become FAIR digital objects that are Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016). By translating the Digital Extended Specimen concept to the local project scale, we provide opportunities to move beyond a theoretical understanding of the DES and towards a practical framework for its implementation. Here we present and discuss the power, limits, and questions in the implementation of the Digital Extended Specimen framework by applying it to the case study of an invasive aquatic plant in the Laurentian Great Lakes region. European frog-bit (Hydrocharis morsus-ranae L.; EFB) is native to western and northern Eurasia and invasive in North America and India. Dense mats of EFB may hinder commercial and recreational use of waterways and decrease light, dissolved oxygen, and native species diversity. We describe a multi-taxonomic study that examined EFB along with associated plant species, animal species, and environmental characteristics (Monfils et al. 2021). The integration of such diverse types of empirical data is a necessary prerequisite for determining the factors associated with EFB establishment, the impacts of EFB on native coastal wetland ecosystems, and the development of suitable management regimes for the conservation of native biodiversity. Data gathered from this study are housed in a local database. In our database, we consider both physical specimens and recorded observations as tokens of concrete occurrences of EFB, which define the base units. These tokens are linked to their collection events, which provide environmental and sampling context, as well as co-occurrences of other taxa including plants, invertebrates, fish, anurans, reptiles, and birds. Digitally linked, these extensions of each digital representation of a collected token provide not only empirical evidence of an EFB occurrence, but also directly connect it with all additionally sampled, derived, and associated information. Through this network of extensions we gain a more holistic understanding of EFB’s species associations, habitats, and ecosystem impacts at the level of populations and communities. The application of the Digital Extended Specimen framework at the project level illustrates how the DES can be used in a real-world context and highlights challenges in translating the concept from a theoretical to a practical perspective.


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