Autonomous perception in forest environments requires accurate detection and segmentation of complex natural objects such as trees, rocks, and terrain features. However, the scarcity of large-scale, annotated forest datasets, especially those with depth and instance segmentation labels, hinders progress in deploying robust deep learning models for forestry applications. In this paper, we present SimForest, a 4K-resolution synthetic RGBD dataset generated using a photorealistic forestry simulator built on Unreal Engine 5. SimForest comprises 5,000 images, each annotated with aligned RGB data, depth maps, instance segmentation masks, and detailed metadata including object poses, terrain depth, camera parameters, and environmental conditions such as season, time, and cloudiness. The virtual scenes are geo-located and seasonally matched to a real forest near Umea, Sweden. To demonstrate the utility of SimForest, we conduct an experimental study involving the detection and segmentation of tree trunks using YOLOv11-based models trained on SimForest data. The evaluation shows strong detection accuracy (mAP@50 of 0.92) and solid segmentation performance (mAP@50 of 0.74). These findings highlight the potential of SimForest as a valuable resource for near-field RGBD perception in forestry and related outdoor robotics applications.

Abstract In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short‐term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.

This study aimed to aid the enormous effort required to analyze phraseological writing competence by developing an automatic evaluation tool for texts.We attempted to measure both second language (L2) writing proficiency and text quality.In our research, we adapted the CollGram technique that searches a reference corpus to determine the frequency of each pair of tokens (bi-grams) and calculates the t-score and related information.We used the Level 3 Corpus of Contemporary American English as a reference corpus.Our solution performed well in writing evaluation and is freely available as a web service or as source for other researchers.

Based on big data training we provide 5-gram language models of contemporary Polish which are based on the Common Crawl corpus (which is a compilation of more than 9,000,000,000 pages from across the web) and other resources.We prove that our model is better than the Google WEB1T n-gram counts and assures better quality in terms of perplexity and machine translation.The model includes lower-counting entries and also de-duplication in order to lessen boilerplate.We also provide POS tagged version of raw corpus and raw corpus itself.We also provide dictionary of contemporary Polish.By maintaining singletons, Kneser-Ney smoothing in SRILM toolkit was used in order to construct big data language models.In this research, it is detailed exactly how the corpus was obtained and pre-processed, with a prominence on issues which surface when working with information on this scale.We train the language model and finally present advances of BLEU score in MT and perplexity values, through the utilization of our model.

In contemporary world, translation becomes a critical need of the time.Parallel dictionaries have now become a most accessible source by humans, but confines are there as they do not offer good quality translation function, because of neologisms and words that are out of vocabulary.To overcome this problem in the usage of statistical translation systems is becoming more and more important in maintaining the eminence and quantity of the training data.But due to the limitations in these systems they have very limited availability for few languages and very limited narrow text areas.The purpose of this research is to bring calculation time up gradation via GPU acceleration, tuning script introduction and the enhancement and improvements in the methodologies of the contemporary comparable corpora mining through re-implementation of analogous algorithms through Needleman-Wunch algorithm.Experiments have been conducted on multiple language data which were extracted on numerous domains from Wikipedia.For the sake of Wikipedia, multiple crosslingual contrasts and comparison were established.Optimistic impact on the both quantity and quality of mined data was observed due to such changes and adaptation.The solution is language independent and highly practical especially for under-resourced languages.