Detection and tracking of small objects like vehicles, aeroplanes and ships from satellite imagery targets several critical applications in urban planning, air and sea traffic management, marine surveillance, disaster prediction and relief, etc. But accurately locating small-objects in large and complex satellite images is highly challenging due to loss of contextual information, blurred objects, diminished sizes, etc. This paper proposes a novel dual attention-integrated YOLO model to accurately detect small-objects in the stand Skyfusion dataset. The selected baseline YOLOv11s is fused with global attention and Efficient channel attention mechanisms at varying feature resolutions for retaining global contextual and spatial information along with channel inter-dependencies. The proposed work applies targeted data augmentation to oversample the Ship class instances and benchmarks the proposed technique against state-of-the-art models with the highest F-score and mAP of 72.4% and 71.3% . With minimal increase in parameters, the proposed model thus serves as a scalable and reliable solution for tiny object detection in challenging aerial imagery.
Biological processes are executed concurrently and on different scales. Modeling of biological processes becomes challenging when the concentrations of biochemicals are imprecise. A tractable formalism for model construction to address imprecision in the concentrations of biochemicals. The model calibration is performed by generating finer models from coarser models that represent the set of sequences of biochemical reactions. Learning of the specifications extracted from the coarser model is applied effectively to construct a finer model. Bounded model checking is performed on the prototype by addressing the uncertainty in the concentration of biochemical reactions. Learning of temporal logic formula is leveraged to address uncertainty in the model. The computational feasibility of the formalism is evaluated on a prototype of the RKIP-inhibited ERK pathway. Results are presented and formalism is promising.
Product discovery is crucial for any retailer. Traditional approaches to achieve this involve understanding queries. Current methods for query understanding require knowledge of product attributes. However, the attributes of interest may not be known beforehand. In addition, these attributes may evolve over time. This study aims to identify significant attributes of the product from descriptions, reviews, and other sources such as articles about the products. A phrase mining technique is used to extract relevant and important words and phrases. Meaningful concepts are generated by clustering these words and phrases. Knowledge of existing product attributes and their corresponding values is utilized as constraints during the clustering process. To uncover new attributes, we utilize topic modeling. This study presents a novel topic modeling framework that explicitly handles constraints. Experimental results from real-world examples and a benchmark dataset demonstrate the efficacy of the framework.
Developing robust conversational agents for e-commerce, particularly for voice-activated product ordering, presents a significant challenge. This paper introduces a lightweight and efficient framework for this task, centered on a compact ( ∼ 1B parameter) Large Language Model (LLM), Gemma-3 1B. The proposed methodology leverages parameter-efficient fine-tuning (PEFT) through Low-Rank Adaptation (LoRA) to specialize the model for semantic parsing, combined with post-training quantization to ensure efficient deployment on both server and mobile platforms. The system is trained on a synthetic dataset of 20,000 spoken shopping requests, enabling it to map diverse user utterances to precise JSON order specifications. Experimental evaluation demonstrates the high efficacy of this approach. The LoRA-tuned model achieves an exact JSON match accuracy of approximately 94.6 ∼ 0.25 s) and feasible on-device latency ( ∼ 2.5 s) when using 8-bit quantization. These findings demonstrate that compact LLMs, when coupled with LoRA-based specialization and quantization, can serve as both accurate and deployable semantic parsers for voice-based commerce applications. The proposed framework not only achieves competitive accuracy with significantly reduced hardware requirements but also establishes a reproducible blueprint for real-world conversational AI deployment.
This study presents a comprehensive computational analysis of a curated dataset of instruction–response pairs within the sustainable fashion domain. The dataset comprises consumer-style queries and expert-style answers, providing a dual perspective view of sustainability discourse in fashion. Using a multi-stage text mining pipeline, we examine linguistic properties, sentiment polarity, sustainability depth, inclusivity attributes, and thematic structures through non-negative matrix factorization (NMF) topic modeling. Sentiment analysis is implemented using a domain-adapted lexicon to capture tone differences between queries and responses, while sustainability depth scoring evaluates the presence of critical concepts such as material choice, care practices, circular economy principles, ethical sourcing, and environmental impact. Inclusivity is assessed through rule-based detection of body-positive, gender-neutral, cultural, and sensory accessibility references. The results reveal a marked positivity bias in responses compared to instructions, with a consistent tone shift towards encouragement and reassurance. Topic modeling identifies ten recurring themes, including materials and fabrics, care and longevity, capsule wardrobe planning, ethics and sourcing, and seasonal dressing. Sustainability depth is highest in material-focused and ethics-related topics, whereas styling and fit-oriented topics show lower integration of environmental principles. Inclusivity coverage is uneven, with cultural and gender-neutral aspects underrepresented. The findings are interpreted in the context of strategic brand communication, consumer education, and policy advocacy.
