Narrative to Trajectory (N2T+): Extracting Routes of Life or Death from Human Trafficking Text Corpora

Climate change and political unrest in certain regions of the world are imposing extreme hardship on many communities and are forcing millions of vulnerable populations to abandon their homelands and seek refuge in safer lands. As international laws are not fully set to deal with the migration crisis, people are relying on networks of exploiting smugglers to escape the devastation in order to live in stability. During the smuggling journey, migrants can become victims of human trafficking if they fail to pay the smuggler and may be forced into coerced labor. Government agencies and anti-trafficking organizations try to identify the trafficking routes based on stories of survivors in order to gain knowledge and help prevent such crimes. In this paper, we propose a system called Narrative to Trajectory (N2T+), which extracts trajectories of trafficking routes. N2T+ uses Data Science and Natural Language Processing techniques to analyze trafficking narratives, automatically extract relevant location names, disambiguate possible name ambiguities, and plot the trafficking route on a map. In a comparative evaluation we show that the proposed multi-dimensional approach offers significantly higher geolocation detection than other state of the art techniques

Creating Geospatial Trajectories from Human Trafficking Text Corpora

Human trafficking is a crime that affects the lives of millions of people across the globe. Traffickers exploit the victims through forced labor, involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a form of human trafficking when the migrant fails to pay the smuggler and is forced into coerced activities. Several news agencies and anti-trafficking organizations have reported trafficking survivor stories that include the names of locations visited along the trafficking route. Identifying such routes can provide knowledge that is essential to preventing such heinous crimes. In this paper we propose a Narrative to Trajectory (N2T) information extraction system that analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation in order to automatically plot trajectories of human trafficking routes. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods

Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics

The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data interpretation by introducing a suite of quantitative metrics designed to validate ice sheet annotation techniques. Focusing on both manual and automated methods, including ARESELP and its modified version, MARESELP, we assess their accuracy against expert annotations. Our methodology incorporates several computer vision metrics, traditionally underutilized in glaciological research, to evaluate the continuity and connectivity of ice layer annotations. The results demonstrate that while manual annotations provide invaluable expert insights, automated methods, particularly MARESELP, improve layer continuity and alignment with expert labels

Mining sensor datasets with spatiotemporal neighborhoods

Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on real-world datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods

Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold

We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages the Variational Autoencoder (VAE) integrated with dynamic thresholding and correlation-based feature clustering. This framework enhances the VAE's ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study's main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.Comment: This work was presented at the 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, 07-12 July 2024, Athens, Greec

Effects of Prior Academic Experience in Introductory Level Data Science Course

Data Science is an in-demand skill in the job market. To meet the demand, universities are offering data science courses or programs. These courses/programs not only give students data science skills, but also awareness of the field, increasing the likelihood of their opting for data science as a career. In this work we look at how the students' prior academic experience affects the outcome of an introductory data science class that requires no pre-requisites. We conducted the study in an undergraduate introductory data science class (IS 296) at the University of Maryland, Baltimore County (UMBC). The course was adapted from University of California Berkeley's Data 8 course. A pre and post survey was conducted to measure four factors in light of Social Cognitive Career Theory (SCCT): self-efficacy, identity, motivation and belonging uncertainty of students as a data scientist before and after taking the class. Our results show that although the course was designed requiring no pre-requisites, students with no prior programming experience and no statistics experience showed decrease in the four factors as compared to students with some prior experience

A review on conventional and modern breeding approaches for developing climate resilient crop varieties: NA

The escalating threat of climate change is a major challenge to global food security. One of the ways to mitigate its impact is by developing crops that can withstand environmental stresses such as drought, heat, and salinity. Plant breeders have been employing conventional and modern approaches to achieve climate-resilient crops. Climate-resilient crops refer to both crop and crop varieties that exhibit improved tolerance towards biotic and abiotic stresses. These crops possess the capacity to maintain or even increase their yields when exposed to various stress conditions, such as drought, flood, heat, chilling, freezing and salinity. Conventional breeding entails selecting and crossing plants with desirable traits, while modern breeding deploys molecular techniques to identify and transfer specific genes associated with stress tolerance. However, the effectiveness of both methods is contingent on the crop species and the targeted stress. Advancements in gene editing, such as CRISPER-cas9  and genomics-assisted breeding, offer new opportunities to hasten the development of climate-resilient crops. These new technologies include Marker Assisted Selection, Genome-Wide Association Studies, Mutation breeding, Transcriptomics, Genomics, and more. The review concludes that these cutting-edge techniques have the potential to enhance the speed and precision of developing crops that can endure the challenges posed by climate change

Breeding of Major Legume Crops through Conventional and Molecular Techniques

Legume crops are universally applicable for human and animal food and sustenance because of their relatively high protein and essential amino acid content. Furthermore, they have been linked to sustainable agriculture, noting their ability to bind to atmospheric nitrogen-fixing bacteria. Despite this, several technical limitations of leguminous crops keep their world production far behind that of cereals. This chapter of the book focuses on current developments in breeding and biotechnology of major legume crops. Conventional breeding has primarily set out to recover a number of vegetative and reproductive traits that are associated with different heritability values, which reflect how susceptible each character is to genetic improvement. In conclusion, legume breeding programs using classical breeding methods and biotechnological tools face a promising boost for further application of knowledge and information that may boost their overall production. In plant breeding, the development of improved crop varieties is limited by very long periods of cultivation. Therefore, to increase crop breeding efficiency, they are using new strategies such as high-throughput phenotyping and molecular breeding tools. In this chapter, recent findings on various aspects of crop improvement, plant breeding practices, to explain the development of conventional and molecular techniques

Biosafety Aspects of Genetically Modified Crops

With the advancement in the field of agricultural biotechnology, many genetically modified crops like Bt- cotton, Bt- brinjal have been developed and commercialised to fulfil the need of the world population. Several biosafety concerns viz., risk to human health, risk to environment, ecological concern o has been raised after the rapid commercialization of GM crops every year across the world. As per Convention on biodiversity (CBD), Biosafety is a term used to describe efforts to reduce and eliminate the potential risk resulting from biotechnology and its product. Though many concerns being raised time to time, strict biosafety guideline must be followed before introducing a GM crop in public domain especially in resource poor developing countries

Small millets: A multifunctional crop for achieving sustainable food security under climate change

Millets, a varied collection of small-seeded crops from the Poaceae family, are re-emerging as a viable alternative for sustainable food and nutritional security in the context of climate change. Historically a staple in India, millet consumption declined during the Green Revolution due to emphasis on rice and wheat. However, their nutritional enrichment and climate resilience are rekindling interest. Over ten millet species, including sorghum, pearl, and finger millet, are cultivated globally and thrive in marginal lands with minimal water and low nutrients. Their C4 photosynthetic pathway enhances water-use efficiency, making them suitable for hot, dry climates. Despite their benefits, millets face challenges, such as consumer preferences for rice and wheat and vulnerabilities to extreme weather events. Nevertheless, they offer significant nutritional advantages, including high levels of dietary fiber, essential amino acids, vitamins, and minerals. India is a leading millet producer, cultivating various types and experiencing a recent production surge. Investigations into the resilience of millets underscore their capacity to endure environmental stresses. Strategies for improving millet crops include conventional breeding, mutation breeding, and advanced techniques like CRISPR-Cas9. Bio-fortification efforts aim to address micronutrient deficiencies, with promising results in finger millet varieties. Advancements in genetic engineering and genome editing tools are revolutionizing millet improvement. The pangenome concept, which explores genetic diversity within species, offers a framework for developing enhanced cultivars. Integrating wild millet varieties into breeding programs can further unlock their potential. Comprehensive policy initiatives supporting millet cultivation, research, and public awareness are crucial for promoting these nutrient-rich grains, enhancing food security, and fostering sustainable agriculture