Spatial phenomena often exhibit heterogeneity across spatial extents and even in proximity. This spatial variation is complex to model, especially for large spatial extents that may vary, for example, ice shelves and sea ice. In this paper, we address this gap and, in particular, highlight its use in understanding linkages between sea ice retreat and Antarctic ice shelf (AIS) melt in the Antarctic. The Antarctic is losing ice at an unprecedented rate. While the role of atmospheric forcing and basal melting on both sea ice retreat and continental ice mass loss has been widely studied, how the retreat of sea ice affects the AIS mass loss has not been investigated. In fact, the link between the two has not been well established yet. Traditional models often treat sea ice and AIS as independent systems, limiting their ability to capture localized linkages and cascading feedbacks. To address this, we propose Spatial-Link, a novel graph-based modeling framework that quantifies spatial heterogeneity across varying spatial extents to capture the linkages between sea ice retreat and AIS melt. Our analysis shows how sea ice retreat evolves over an oceanic grid and gradually progresses to the ice shelf - establishing a direct linkage. To the best of our knowledge, this is the first-ever direct linkage methodology between sea ice retreat and AIS melt events. By integrating dynamic cryospheric processes into a unified framework, Spatial-Link offers a scalable, data-driven tool for improving the accuracy of sea-level rise projections and informing targeted climate adaptation strategies.

Traditional statistical analyses do not reveal the spatial locations and the temporal occurrences of clusters of anomalous events that are responsible for a significant loss of sea ice extent. To address this problem, we present a novel method named Convolution Matrix Anomaly Detection (CMAD). The onset and progression of clusters of anomalous melting events over the Antarctic Sea ice are studied as loss in sea ice extent, which are essentially negative values, where the traditional convolutional operation of the Convolutional Neural Network (CNN) approach is ineffective. CMAD is based on an inverse max pooling concept in the convolutional operation of CNN to address this gap. CMAD is developed to offer a solution without using a neural network, and unlike a full CNN, it doesn't require any training or testing processes. Satellite images are utilized to establish the loss in the Antarctic region. Our analysis shows that anomalous melting patterns have significantly affected the Weddell and the Ross Sea regions more than any other regions of the Antarctic, consistent with the largest disappearance in sea ice extent over these two regions. These findings bolster the applicability of the inverse max pooling based CMAD in detecting the spatiotemporal evolution of clusters of anomalous melting events over the Antarctic region. The anomalous melting process was first noticed along the outer boundary of the sea ice extent in early September 2022 and gradually engulfed the entire sea ice region by February 2023 -in tandem with the scientific literature. These findings indicate that there is a necessity to delve deeper into the role of the anomalous melting process on sea ice retreat for a better understanding of the sea ice retreat process. The nature of the problem is to detect clusters of contiguous grids of anomalous melting events rather than detecting discrete grid points. CMAD's ability to perform both data clustering and anomaly detection via the pooling operations allows for a more comprehensive analysis of sea ice melt patterns, facilitating the pinpointing of areas with potentially significant melt events. This method has the potential to apply in other fields of study where anomalous events are detected in clusters. The inverse max pooling concept has successfully detected clusters of anomalous events in sea ice and demonstrated the capability to detect anomalies with 87% accuracy in benchmark data. In contrast to well-established conventional methods such as DBSCAN, HDBSCAN, K-Means, Bisecting K-Means, BIRCH, Agglomerative Clustering, OPTICS, and Gaussian Mixtures, when applied to dynamic multidimensional data, CMADBenchmark (which is a variation of CMAD) exhibits superior capabilities in detecting extreme events. The comparative analysis reveals that CMADBenchmark outperforms these traditional approaches, showcasing its heightened sensitivity and efficacy in capturing significant variations within evolving multidimensional datasets over time. This heightens the detection accuracy positions of CMAD as a valuable tool for discerning extreme events in the context of dynamic and changing multidimensional data.

EcoPro is a digital twin to perform an ecological projection which is to predict changes in ecosystems in response to environmental drivers. EcoPro provides a platform to access Earth Systems Model (ESM) outputs and Earth Systems observation datasets, to develop an ecological model that connects ecosystem predictors with environmental drivers, to downscale the ESM model outputs to the resolution relevant to the ecosystem, to apply the ecological model to the downscaled ESM model outputs, to visualize the ecological projection results to the high resolution to inform application users and decision makers, and to assess the performance of new observing systems for ecological projection. We present the design and implementation of EcoPro and the scientific use cases we studied with EcoPro.

Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.

In cloud computing consumers often seek some assurance from cloud service providers (CSPs) that services will be provided according to consumers' requirements. The ''service-level-agreement'' (SLA) between a CSP and a consumer solves this issue to an extent and provides some level of assurance. However, SLAs alone do not solve the issue entirely as there is no unique rule to create an SLA. They vary in description, length, and types of information released. Therefore, consumers need a better way of estimating 'trustworthiness' of a cloud (CSP). In this work we propose a framework to alleviate the above issue. Our framework estimates trustworthiness of a cloud using a quantitative model of trust. We identified and formalized several parameters that can be extracted from SLA or retrieved during the sessions and are used to estimate trust.

Conventional access control are suitable for regulating access to resources by known users.However,these models have often found to be inadequate for open and decentralized multi-centric systems where the user population is dynamic and the identity of all users are not known in advance.For such systems, credential based access control has been proposed. Credential based systems achieve access control by implementing a binary notion of trust.If a user is trusted by virtue of successful evaluation of its credentials it is allowed access, otherwise not. However,such credential based models have also been found to be lacking because of certain inherent drawbacks with the notion of credentials.In this work,we propose a trust based access control model called TrustBAC. It extends the conventional role based access control model with the notion of trust levels.Users are assigned to trust levels instead of roles based on a number of factors like user credentials,user behavior history,user recommendation etc. Trust levels are assigned to roles which are assigned to permissions as in role based access control.The TrustBAC model thus incorporates the advantages of both the role based access control model and credential based access control models.