Abstract

Attribute-based encryption (ABE) is widely used for a secure and efficient data sharing. The predetermined access policy of ABE shares the data with intended data users. However, ABE is not preferable in many applications that require collaboration among data users. In such applications, an authorized data user may be interested to collaborate with another data user who does not adhere to the access policy. Fixed access policy of ABE does not allow an authorized data user (who satisfies the access policy) to collaborate or share the data with any unauthorized data user (who fails to satisfy the access policy). Thus, due to the static and predefined access policy, data collaboration in ABE is significantly challenging. In this work,

Abstract

Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which com- bines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatol- ogy. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub- humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics com- pared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates.

Abstract

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are fre- quently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer lan- guage model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and appli- cations using LLMs, we publicly release our models and code under the Responsible AI License.1 Keywords: Language models, collaborative research

Abstract

Radiance Fields (RFs) have shown great potential to represent scenes from casually captured discrete views. Compositing parts or whole of multiple captured scenes could greatly interest several XR applications. Prior works can generate new views of such scenes by tracing each scene in parallel. This increases the render times and memory requirements with the number of components. In this work, we provide a method to create a single, compact, fused RF representation for a scene composited using multiple RFs. The fused RF has the same render times and memory utilizations as a single RF. Our method distills information from multiple teacher RFs into a single student RF while also facilitating further manipulations like addition and deletion into the fused representation.

Abstract

Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment which helps them to navigate on-demand when given a linguistic instruction. A natural goal in Vision Language Navigation (VLN) research is to impart autonomous agents with similar capabilities. Recently introduced VL Maps \cite{huang23vlmaps} take a step towards this goal by creating a semantic spatial map representation of the environment without any labelled data. However, their representations are limited for practical applicability as they do not distinguish between different instances of the same object. In this work, we address this limitation by integrating instance-level information into spatial map representation using a community detection algorithm and by utilizing word ontology learned by large language models (LLMs) to perform open-set semantic associations in the mapping representation. The resulting map representation improves the navigation performance by two-fold (233\%) on realistic language commands with instance-specific descriptions compared to VL Maps. We validate the practicality and effectiveness of our approach through extensive qualitative and quantitative experiments.

Abstract

Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still, automated factual inconsistency detection is rather under-studied. Existing work has focused on (a) finding fake news keeping a knowledge base in context, or (b) detecting broad contradiction (as part of natural language inference literature). However, there has been no work on detecting and explaining types of factual inconsistencies in text, without any knowledge base in context. In this paper, we leverage existing work in linguistics to formally define five types of factual inconsistencies. Based on this categorization, we contribute a novel dataset, FICLE (Factual Inconsistency CLassification with Explanation), with ~8K samples where each sample consists of two sentences (claim and context) annotated with type and span of inconsistency. When the inconsistency relates to an entity type, it is labeled as well at two levels (coarse and fine-grained). Further, we leverage this dataset to train a pipeline of four neural models to predict inconsistency type with explanations, given a (claim, context) sentence pair. Explanations include inconsistent claim fact triple, inconsistent context span, inconsistent claim component, coarse and fine-grained inconsistent entity types. The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities). We experiment with multiple Transformer …

Abstract

Perception of interval timing influences the behaviour of the organisms. Computational models of interval timing are categorized into Pacemaker Accumulator models, Memory-based models, Oscillator models and Random Process models, Ramping Activity models and Population Clock models. Random process models or drift diffusion models are biologically plausible models and are based on the activity of spiking neurons. In this paper, we proposed a computational model of interval timing based on spiking neurons. The results are validated against the Scalar property of interval timing

Abstract

Understanding user perspectives on Artificial Intelligence (AI) in education is essential for creating pedagogically effective and ethically responsible AI-integrated learning environments. In this paper, we conduct an extensive qualitative content analysis of four major social media platforms (Twitter, Reddit, YouTube, and LinkedIn) to explore the user experience (UX) and perspectives of early adopters toward ChatGPT-an AI Chatbot technology-in various education sectors. We investigate the primary applications of ChatGPT in education (RQ1) and the various perceptions of the technology (RQ2). Our findings indicate that ChatGPT is most popularly used in the contexts of higher education (24.18%), K-12 education (22.09%), and practical-skills learning (15.28%). On social media platforms, the most frequently discussed topics about ChatGPT are productivity, efficiency, and ethics. While early adopters generally lean toward seeing ChatGPT as a revolutionary technology with the potential to boost students' self-efficacy and motivation to learn, others express concern that overreliance on the AI system may promote superficial learning habits and erode students' social and critical thinking skills. Our study contributes to the broader discourse on Human-AI Interaction and offers recommendations based on crowd-sourced knowledge for educators and learners interested in incorporating ChatGPT into their educational settings. Furthermore, we propose a research agenda for future studies that sets the foundation for continued investigation into the application of ChatGPT in education.

Abstract

It is a well-established fact that strategic placement of items on the shelves of a retail store significantly impacts the revenue of the retailer. Consumer goods sold in retail stores can be classified into essential and typically low-priced convenience items, and non-essential and high-priced shopping items. Notably, the lower-priced convenience items are critical to ensuring consumer foot-traffic, thereby also driving the sales of shopping items. Moreover, users typically buy multiple items together (i.e., itemsets) to facilitate one-stop shopping. Hence, it becomes a necessity to strategically index and place itemsets that contain both convenience items and shopping items. In this regard, we propose a consumer-good-type aware and revenue-conscious itemset indexing scheme for efficiently retrieving high-revenue itemsets containing both convenience and shopping items. Moreover, we propose an itemset placement …

Abstract

Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird’s Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmen- tation has emerged as a prominent approach in autonomous driving. However, the current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map. The trajectory samples are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty in the cost maps that can arise due to noise in RGB images, BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, merging, etc from the neighboring vehicles. In this paper, we propose UAP-BEV, a novel approach that models the noise in Spatio-Temporal BEV predictions to create an uncertainty-aware occupancy grid map. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map