Perubahan Pola Iklim dan Pengaruhnya terhadap Waktu Panen Duku (Lansium domesticum Corr.)

Sugiarto A, Tanjung RD, Pratama R 2022. Changes in climate patterns and their effects on harvest time of duku (Lansium domesticum Corr.). In: Herlinda S et al. (Eds.), Prosiding Seminar Nasional Lahan Suboptimal ke-10 Tahun 2022, Palembang 27 Oktober 2022. pp. 859-870. Palembang: Penerbit & Percetakan Universitas Sriwijaya (UNSRI).Climatic factors are essential in the fruit formation and development process of seasonal fruits, such as duku (Lansium domesticum Corr.). This study aimed to determine changes in climate patterns and their relation to the harvest time of duku. This research takes climate study data from 2018 and 2021, the processing and analysis of data to see climate patterns. Observation the harvest time of duku took a case study on plantations located in three villages (Berkat, Kijang Awal Terusan, and Serdang Menang), Sirah Pulau Padang District, Ogan Komering Ilir Regency. The results show that the climate conditions in 2018 and 2021 are not much different, but the climate patterns are very different. The harvest time under climatic conditions in 2018 is in January-February 2019, while the harvest time under climatic conditions in 2021 is in October-November 2021, January 2022, and March 2022. Observation of the harvest time in three villages under climatic conditions in 2018 looks the same, but the harvest time for 2021 climatic conditions is different. The harvest time in Berkat Village is in October and January, Kijang Awal Terusan in January and March, and Serdang Menang in November, January, and March. These results indicate that changes in climate patterns will affect the harvest time of duku

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning.Comment: Accepted by AAAI 202