Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have yet to be compared and their synergy remains unexplored. In this paper, we identify different strengths and weaknesses through evaluating state-of-the-art models on benchmark datasets: Text-to-SQL demonstrates superiority in handling questions involving arithmetic operations and long tables; E2E TQA excels in addressing ambiguous questions, non-standard table schema, and complex table contents. To combine both strengths, we propose a Synergistic Table-based Question Answering approach that integrate different models via answer selection, which is agnostic to any model types. Further experiments validate that ensembling models by either feature-based or LLM-based answer selector significantly improves the performance over individual models.

Environmental monitoring of lakeside green areas is crucial for environmental protection. Compared to manual inspections, computer vision technologies offer a more efficient solution when deployed on-site. Multispectral imaging provides diverse information about objects under different spectrums, aiding in the differentiation between waste and lakeside lawn environments. This study introduces WasteMS, the first multispectral dataset established for the semantic segmentation of lakeside waste. WasteMS includes a diverse range of waste types in lawn environments, captured under various lighting conditions. We implemented a rigorous annotation process to label waste in images. Representative semantic segmentation frameworks were used to evaluate segmentation accuracy using WasteMS. Challenges encountered when using WasteMS for segmenting waste on lakeside lawns were discussed. The WasteMS dataset is available at https://github.com/zhuqinfeng1999/WasteMS.

This study intends to build a smartbin that segregates solid waste into its respective bins. To make the waste management process more interesting for the end-users; to notify the utility staff when the smart bin needs to be unloaded; to encourage an environment-friendly smart bin by utilizing renewable solar energy source. The researchers employed an Agile Development approach because it enables teams to manage their workloads successfully and create the highest-quality product while staying within their allocated budget. The six fundamental phases are planning, design, development, test, release, and feedback. The Overall quality testing result that was provided through the ISO/IEC 25010 evaluation which concludes a positive outcome. The overall average was 4.55, which is verbally interpreted as excellent. Additionally, the application can also independently run with its solar energy source. Users were able to enjoy the whole process of waste disposal through its interesting mechanisms. Based on the findings, a compressor is recommended to compress the trash when the trash level reaches its maximum point to create more rooms for more garbage. An algorithm to determine multiple garbage at a time is also recommended. Adding a solar tracker coupled with solar panel will help produce more renewable energy for the smart bin.

This paper deals with extracting and learning patterns of assonance as a stylistic feature for author attribution of historical texts. We describe an assonance extraction algorithm, and consider results from an extensive set of machine learning experiments, based on a historical corpus of 18th century American and British texts. The results are compared with those obtained from the use of other prosodic and traditional stylistic features.