dataesr/scientific-paragraphs-categorization huggingface dataset:
Results revealed that the potential annual soil loss from the hill tracts was higher in 2021 than 2017. Although, total annual rainfall was high in 2017, soil loss was found higher in 2021 which is an indication of land use changes. This investigation will help the policy makers to identify the erosion vulnerable areas that requires immediate soil conservation practices. However, there is no latest eld-based data available for the country which is required to validate the estimated soil loss of the model. Hence, it is recommended to conduct eld-based researches for validating the modelderived result, developing soil erosion model, and creating a reliable soil erosion database for the country.
Data acquisition In this study, 30 years data of each meteorological station located in the studied area was obtained from the Bangladesh Meteorological Department (BMD). There were total 9 meteorological stations (Fig. 1 ), and the obtained data was used to determine the erosivity of the rainfall in the area. The digital elevation, which was used to estimate the slope length, slope steepness, and P factor was downloaded from the o cial website of USGS (https://earthexplorer. usgs. gov). Land use and cover was required to determine the cover and management factor in the study area. Land use and land cover data from Sentinel-2 with a 10-meter resolution was downloaded from ESRI website (https://livingatlas. arcgis. com/landcover). The vector database of the soil in the region was obtained from the Harmonized World Soil Database (https://www.
Due to the lack of in situ soil erosion database, we could not validate our estimation. However,  Hossain et al. , (2023)  conducted an investigation on soil erosion in the Cox's Bazar district of the Chittagong division in 2015 and 2020, and they found that the mean soil loss in the studied area was 58. 2 and 59 ton ha - 1 year - 1 , in 2015 and 2020, respectively. On the other hand, in the camp of geopolitical refugees (Rohingya refugees), the soil loss was 59 ton ha - 1 year - 1 in 2015 and 78 ton ha - 1 year - 1 in 2020. They also con rmed that the higher topography and rugged terrain in this region could be the reason of causing high soil erosion. Our estimation corresponds with their ndings. The total and mean soil loss in the southeastern region was high compared to the northeastern hill tracts.
Hence, the application of this model in the areas outside the range and to the non-plot-level might not represent an accurate soil loss estimation  (Borrelli et al. , 2013; Nearing et al. , 2000). Soil erosion estimation using empirical-based models has several drawbacks, and many studies lack proper validation due to the lack of regional database  (Borrelli et al. , 2021). The validation of our soil loss estimation was di cult as there is neither any recent eld-level data nor model-derived database on soil erosion for Bangladesh.
72, 107. 83, and 114. 04 ton ha - 1 year - 1 in the southeast region. This increasing trend of soil loss from 2017 indicates the rapid change of land cover in these areas. This assessment was solely based on open and freely available database. There was neither any eld observation nor a robust soil erosion database or su cient research works on soil erosion for the country to validate our ndings which was the key limitation in this research. Additionally, there was also a lack of research data regarding the local agricultural and conservation practices. Even the existing soil erosion data from eld observation is too old. However, our estimated soil loss value is still consistent with a few scienti c works that were conducted in a smaller scale. Since soil erosion by water is one of the major causes of land degradation in Bangladesh, it is essential to develop a reliable soil erosion database so that the government can take necessary steps to minimize soil erosion and encourage the farmers to adopt conservation practices.
SBO algorithms approximate the black-box function f in every iteration with a surrogate model g (the online learning problem), then optimize g instead (the optimization problem). Both the results of learning and optimization become better with each simulator call as more data becomes available. The problem is typically split into two sub-problems that are solved every time a tour is given as input to the simulator: 1. Given the tours tried up until now and their corresponding rewards, learn a model to predict how promising any new tour would be. 2. Optimize the model of the previous step to suggest the most promising tour to try next. Then this tour is given as input to the simulator. The first step can be seen as an online learning problem, where new data comes in at every iteration, and rewards need to be predicted.
Problem instances A set of problem instances was generated and provided for the participants. Each problem instance contains n nodes in 2D space. Each node has an x-coordinate, a y-coordinate, lower and upper bounds of its time window, prizes, and the maximum tour time. The source code of the competition was provided to participants. Thus, everyone had access to how instances and environments were generated, and participants could inspect each of its components. The implementation of the source code of the competition was done using the Python programming language. In more detail, the InstanceGenerator (Algorithm 1) class generates the features of the problem instances. This class is initialized by the number of nodes, limits of x and y coordinates, w, and a random seed. The problem instances are generated in three steps.
Submissions In both tracks, the participants were asked to submit their output files containing the result of their proposed method as well as their implementation code for inspection by the organizing team. In Track 1, the submission file consisted of a single tour. This tour was used to compute the average performance over 10,000 Monte Carlo samples. In Track 2, participants were supposed to submit a single file containing the tours for each instance (1,000) and each Monte Carlo sample (100). In total, 100,000 tours should have been submitted for evaluating performance. This file was then used to compute the overall performance of the submission. In both cases, participants had to submit tours with size n + 1, where a visit to the depot determined the end of the tour.
The GA has managed to successfully find better solutions than the distinct calls to the iterative approach. We found a number of solutions scoring 11. 32, which is the optimal solution for the Track 1 test instance, after running the GA for 2 hours in the previously described setup. Fig. 2  presents the estimated probability that a node occurs in a certain position of the optimal solution. These estimates are based on the optimal solutions found through the GA. Note that in Fig. 2  the optimal solutions share the exact same set of nodes; moreover, the order of visit of some neighboring nodes can be swapped. Further details about the method and how it has been implemented can be found on GitHub. 4.
In the last step, the best 250 solutions from the cache memory, which contain the best from the last run of the EA, are evaluated with a fidelity of 10 000 evaluations and the best one is returned. Experiments were carried out on a cluster of 2 × 8-core Intel Xeon E5-2650v2 2. 60 GHz with 64 GB RAM. We launched multiple independent runs of the above algorithm in parallel to assess the robustness of the results, however, each run is executed sequentially on a single core. A complete run of the above algorithm requires an average of 12 hours to complete. The code is made available on Zenodo. 5.
RISE up Our solution approach for the challenge consists of three components. First, we use the POMO reinforcement learning approach proposed by Kwon et al. [9]  to learn one policy per problem size. Next, we use efficient active search  [52]  to fine-tune the learned policies for each instance being solved, thus creating an individualized policy for each instance of the test set. Finally, we use Monte-Carlo rollouts to construct the final solutions. In the following, we will describe each of these three components in more detail. The code for our method and the trained models are available online. 6 POMO We use POMO to learn an initial policy for each problem instance size. The POMO approach is an end-to-end deep reinforcement learning approach for combinatorial optimization problems. POMO is based on the REINFORCE algorithm but exploits symmetries in combinatorial optimization problems to encourage exploration during learning.
In each epoch, 6 simulations on the same instance were performed, each one composed of 32 sample solutions, equaling a batch size of 192 sampled solutions. Further implementation details and hyperparameter values can be found in GitHub. 7 Inference During evaluation, solutions are constructed using a greedy strategy, i. e. at each time step, we select the node for which the model gives the highest probability. All experiments were performed on a 6 core CPU at 1. 7 GHz (Intel Xeon CPU E5-2603 v4) with 12 GB of RAM and an Nvidia GeForce GTX 1080 Ti GPU. The final model took up to 48 hours to train and around 12 hours to generate the validation/test submission files.
Simulated Annealing is selected as the acceptance criterion for the search with a starting temperature of 1 and a linear decay step of 0. 01 for cooling, bounded at 0. 25. We use the original scoring function of ALNS and update weights using convex combinations of the current weights and the score obtained for the current candidate solution. Search is performed for 100 iterations, regardless of the instance size. This baseline was implemented after the competition and was therefore not available to participants. The code for the implemented ALNS is available online. For track 1, if participants manage to develop an SBO approach tailored to the problem at hand, e. g. by dealing with the constrained combinatorial search space, the expectation is that it can outperform classical heuristic approaches that do not make use of machine learning, such as ALNS.
The reason for this is that a surrogate model deals with two aspects of the problem at the same time: 1) the expensive objective, and 2) the stochasticity. By replacing the objective with a deterministic and cheap to compute surrogate, these aspects of the problem are circumvented. Such a method would still need to evaluate the expensive objective but is better suited for a low number of these evaluations than traditional heuristic approaches  [34]. For track 2, we expect new methods based on DRL to be able to outperform the ALNS for this problem. The reason for this is that several works have proven that DRL can find better heuristics through learning  [14, 15] , while classical heuristics make decisions based on hand-crafted rules. If enough data and computation time for learning is available, these learning capabilities could be leveraged to improve upon hand-crafted heuristics.
Another aspect of the competition that was beneficial for everyone was a chat function, where any confusion was quickly cleared up. There are also points of improvement for future (similar) competitions. It remains questionable whether providing the whole simulator to participants is really the way to go in an ML competition. Although the samples of random variables in the simulator were not known in advance, the exact probability distributions could be retrieved by looking at the code to which participants had access. This was known by the organizers, but it was decided to not obfuscate this information so that it remained easy for participants to run the provided simulator and use it for ML. Another point of improvement is the difficulty of the problem, especially for Track 1. Because we did not want to slow down the solution evaluation process on our server too much, and because we noticed solutions in the validation phase were still being improved, we decided to keep the instance size somewhat limited.
Conclusion We reported on the first international competition on AI4TSP. The participants were asked to solve an orienteering problem with stochastic weights and time windows (OPSWTW). We described the setup of two tracks, focusing on two learning techniques from AI: surrogate-based optimization and deep reinforcement learning. We described the approaches of the winning teams of two tracks and gave an overview of the results. Furthermore, we explained the developed simulation model, which was used to generate the training and testing instances of OPSWTW, and was coupled with two implemented baseline algorithms of two tracks. In addition, the simulator used in this competition and the code of some of the winning approaches of both tracks of the competition has been made publicly available. The simulation model with various algorithms can serve as a benchmark for researchers to develop and compare surrogate-based and reinforcement learning-based approaches to stochastic routing problems.
This study aims to promote the protection and inheritance of cultural heritage in private gardens in the Jiangnan area of China. By establishing a precise visual labeling system and accelerating the construction of a database for private garden features, we deepen the understanding of garden design philosophy. To this end, we propose an improved Jiangnan private garden recognition model based on You Only Look Once (YOLO) v8. This model is particularly suitable for processing garden environments with characteristics such as single or complex structures, rich depth of field, and cluttered targets, effectively enhancing the accuracy and efficiency of object recognition. This design integrates the Diverse Branch Block (DBB), Bidirectional Feature Pyramid Network (BiFPN) , and Dynamic Head modules (DyHead) to optimize model accuracy, feature fusion, and object detection representational capability, respectively.
The enhancements elevated the model's accuracy by 8. 7%, achieving a mean average precision (mAP) value of 57. 1%. A specialized dataset, comprising 4890 images and encapsulating various angles and lighting conditions of Jiangnan private gardens, was constructed to realize this. Following manual annotation and the application of diverse data augmentation strategies, the dataset bolsters the generalization and robustness of the model. Experimental outcomes reveal that, compared to its predecessor, the improved model has witnessed increments of 15. 16%, 3. 25%, and 11. 88% in precision, mAP0. 5, and mAP0. 5:0. 95 metrics, respectively, demonstrating exemplary performance in the accuracy and real-time recognition of garden target elements. This research not only furnishes robust technical support for the digitization and intelligent research of Jiangnan private gardens but also provides a potent methodological reference for object detection and classification research in analogous domains.
To overcome these challenges, without the foundation of an open database, we have created a new database containing 4890 images and have categorized the numerous garden elements into four categories: architecture, stone bridges, plants and flowers, and artificial mountains. To augment the precision and robustness of detection, the following enhancements were made to the initial structure of YOLOv8: (1) The Diverse Branch Block branch block (DBB) module is added to the backbone layer, replacing the Conv in Bottleneck in C2f, to enhance the model's precision. (2) A bidirectional feature pyramid network (BiFPN) module is adopted at the neck level to replace the original feature pyramid module, thereby achieving efficient bidirectional cross-scale connections and weighted feature fusion. (3) The dynamic head (DyHead) module is added to the head layer to enhance the representational capability of the object detection head.
Following these improvements, the results indicate that YOLOV-modify improved the accuracy by 8. 7% compared to the original YOLOV8, with a mean average precision (mAP) value reaching 57. 1%. The subsequent sections of the paper are organized as follows. Section II introduces the development history of object detection algorithms and the current status and challenges of Jiangnan private garden research. Section III provides a detailed description of the network structure of YOLOV8 and its improved components. Section IV elucidates the collection and processing of the dataset and the parameter settings for model training. Section V presents the results of the experiments. Section VI conducts research discussions, while Section VII explores the prospects and shortcomings of the research.
While achieving substantial improvements in object detection accuracy, their computational demands render them unsuitable for real-time applications. Particularly, the YOLO series of algorithms have emerged as prominent in one-stage object detection algorithms, achieving real-time object detection by transforming the object detection task into a dense regression problem. YOLOv8, released in 2023, integrates various cutting-edge technologies, enhancing the accuracy and robustness of detection  [22] [23] [24] [25] [26]. 2. 2 Pressing Issues for Jiangnan Private Garden Object Detection Based on YOLOv8 (1) Dataset Dilemma: The absence of a public dataset specifically tailored for Jiangnan private gardens necessitates the construction of a large-scale dataset encompassing various landscape elements.
Data Collection and Preprocessing To facilitate the study of the Jiangnan private gardens, a specialized large-scale dataset must be constructed, given the absence of a public dataset for Jiangnan private gardens. Initially, 3280 representative images of the Jiangnan private gardens were sourced from the internet, covering gardens such as the "Four Famous Gardens": Nanjing Zhan Garden, Suzhou Liu Garden, Suzhou Zhuozheng Garden, and Wuxi Jichang Garden, and other gardens such as Shanghai Yu Garden, Yangzhou Slender West Lake, Ge Garden, He Garden, Suzhou Canglang Pavilion, Lion Forest Garden, and Nantong Shuihui Garden, etc. Additionally, 1610 photos were personally captured in Jiangnan private gardens, constructing a database containing 4890 images (Figure  6 ). These images span four categories; to enhance the robustness of the model, 1% of background images were also added, ensuring the images display various angles and different lighting conditions of the gardens.
The dataset was randomly divided using the holdout method, with a split ratio of test set: training set: validation set = 80%: 10%: 10%. Ultimately, there were 3912 training images, 489 test images, and 489 validation images were obtained. Firstly, the size of the images was adjusted to dimensions like 640x640, and pixel values were normalized between 0 and 1 to enhance the training effect and stability of the model. Secondly, data augmentation was performed using several methods to enrich the dataset.
Dataset Annotation Accurate dataset annotation is critical for successful object detection. Rectangular boxes were utilized to annotate each object, and the corresponding class labels were assigned to each box. The architecture was labeled JZ, stone bridges SQ, rockeries JS, and plants ZW. The LabelImg tool was used for manual annotation to enhance the efficiency and accuracy of annotation  [60]. During annotation, particular attention was paid to ensure that each object's bounding box was tight, the issues of vegetation occlusion and architectural segmentation were addressed, and buildings of multiple categories were annotated separately. For each image, corresponding annotation files were generated, such as YOLO format label files (each line contains a target's class and bounding box coordinate information) or COCO format label files (each file contains all the target information for one image).
Data Preprocessing and Augmentation Methods The dataset was randomly divided using the holdout method, with a split ratio of test set: training set: validation set of 80%: 10%: 10%. Ultimately, 3912 training images, 489 test images, and 489 validation images were obtained. First, the size of the images was adjusted to dimensions like 640x640, and the pixel values were normalized between 0 and 1 to enhance the training effect and stability of the model. Second, data augmentation was performed using several methods to enrich the dataset. (1) A total of 35% of the images were mirror-flipped to increase image diversity and reduce the dependence of the model on mirror symmetry. (2) 25% of the images were randomly flipped to enhance the ability of the model to recognize objects at different angles.
Computer Configuration and Parameter Settings In our experimental environment, we used the YOLOv8 model developed by Ultralytics, version 0. 114. The experimental environment was configured using Python 3. 9. 0, VScode (1. 76. 0) IDE, and CUDA 10. 2. All model training and testing were executed on an NVIDIA TITAN V (12 GB). During the training phase, the optimizer choice (optimizer: auto) was left adaptive, automatically selecting between SGD and Adam and adapting to the gradient descent characteristics of different tasks. The SGD initial learning rate was set to 1E-2, the Adam initial learning rate to 1E-3, and the weight decay to 5E-4. The weight decay and momentum factors were set to 0. 0005 and 0. 937, respectively. In addition, three warm-up stages of 0. 8 momentum were conducted, and the cosine annealing method was utilized to decay the learning rate, with 500 epochs per experiment and a batch size of 16.
Metrics for Evaluation To verify the efficacy of the modified YOLOv8 model, herein referred to as YOLOv8-modify, using our curated dataset, we used four prevalent metrics: Precision, Recall, F1 Score, and mAP, complemented by the confusion matrix to gauge the model's performance. An Intersection Over Union (IOU) threshold of 0. 7 and a confidence threshold of 0. 25 were established to impartially assess experimental outcomes.
Ablation Study To substantiate the optimization impact of the three enhancement strategies on the garden dataset, ablation studies were conducted to assess the efficacy of each enhancement strategy. The experimental results are presented in Table  1. Upon integrating the three modules into the network model for the ablation experiments, DBB enhanced the detection accuracy without increasing the model complexity and computational burden. The BiFPN efficiently fuses features, whereas DyHead overlays scale, spatial, and task attention, thereby fortifying the model's feature extraction capabilities and enhancing all metrics. Table  1  displays the results of all tests, wherein each model, set with consistent hyperparameters and pretraining weights, was trained for 500 epochs. The findings indicated that the application of the three module modifications to the optimized YOLOv8 achieved a precision of 66.
7%, and an mAP50 of 57. 1%. This significant enhancement in accuracy validates the efficacy of the proposed method for object  Figure  11  illustrates that the recall rates for all models range between 0. 467 and 0. 535. YOLOv8n achieves a peak recall of 0. 535 without incorporating additional modules. Conversely, the introduction of multiple modules to YOLOv8 (denoted as YOLOv8-modify) resulted in a reduced recall of 0. 467, indicating an increase in detection accuracy while potentially overlooking certain detectable objects. This phenomenon is partially attributed to the actual image content because numerous images in the database are densely populated with greenery, buildings, and various visually complex detection objects. Although some missed detections were reasonable, our algorithm was initially configured to prioritize high detection accuracy.