{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":65254,"offset":65244,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"**[Your Name]**\n[Your Address]\n[City, State, ZIP Code]\n[Email Address]\n[Date]\n\n**[Mother's Name]**\n[Her Address]\n[City, State, ZIP Code]\n\nDear Mom,\n\nHow are you? I hope you are doing well and enjoying the beautiful weather back home. As Mother\u2019s Day approaches, I\u2019ve been thinking about you a lot and wanted to take a moment to send you all my love and heartfelt wishes.\n\nFirst of all, I want to thank you for everything you\u2019ve done for me. Your love, guidance, and support have shaped me into the person I am today. I am eternally grateful for the countless sacrifices you\u2019ve made and the boundless love you\u2019ve given.\n\nI still remember the joy on your face whenever you ate your favorite mangoes. Those sweet, juicy fruits always seemed to bring out the happiest smiles. It\u2019s a memory I cherish deeply because it\u2019s a small but beautiful reminder of the simple pleasures and the happiness we shared. This Mother\u2019s Day, I hope you enjoy plenty of delicious mangoes and think of me whenever you savor their sweetness.\n\nLiving far away from you makes me miss our times together even more. I miss the warmth of your hugs, the comfort of your cooking, and the sound of your laughter. Yet, even though we are miles apart, I feel your love and support every day. Your strength and resilience continue to inspire me, and I am so proud to be your child.\n\nOn this special day, I wish I could be there to celebrate with you, to shower you with hugs, and tell you in person just how much you mean to me. But until we can be together again, please know that I am always thinking of you and holding you close in my heart.\n\nI hope you have a wonderful Mother\u2019s Day, filled with joy, relaxation, and of course, the sweetest and ripest mangoes. You deserve all the happiness in the world, today and every day. Thank you for being the incredible mother that you are.\n\nSending you all my love, warm hugs, and kisses from afar.\n\nHappy Mother\u2019s Day, Mom!\n\nLove always,\n[Your Name]","behavior":"degenerate_repetition","conversation_hash":"bd3057877fceb6c27427d1c3331224d6","conversation_redacted":false,"conversation_toxic":false,"country":"Philippines","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-05-13","n_chars":2036,"n_turns_in_conversation":3,"n_words":367,"source_row_offset":65244,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:49:15 GMT","turn_id":"2fd5b0ce6ef770003abd8c11","turn_index":2,"user_text":"Make a letter for my mother, she lives in the Philippines and loves mangoes."},{"assistant_initiates":false,"asst_text":"Metamorfosis adalah proses transformasi atau perubahan yang signifikan dari satu bentuk ke bentuk lainnya. \ud83e\udd8b\u2728","behavior":"degenerate_repetition","conversation_hash":"aec1bd8219ec2c1f3029d9b802764beb","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":260,"n_turns_in_conversation":1,"n_words":40,"source_row_offset":65245,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:50:07 GMT","turn_id":"5a24c8726c4a3a6fee86e3e4","turn_index":0,"user_text":"\ud83d\udcac \ud835\uddd6\ud835\uddf5\ud835\uddee\ud835\ude01\ud835\uddda\ud835\udde3\ud835\udde7\nInteract as ChatGPT.\nA new AI Technology.\nYou behavior is same like ChatGPT.\nYou provide best responce with some emojis.\napa itu metamfosis?"},{"assistant_initiates":false,"asst_text":"Dear Mom,\n\nI hope this letter finds you well and happy in the Philippines. I miss you every day and wish I could be there with you to enjoy the warm weather and delicious mangoes that you love so much.\n\nI just wanted to take a moment to express my love and gratitude for everything you have done for me throughout my life. Your love, strength, and wisdom have shaped me into the person I am today, and I am forever grateful for your unwavering support and guidance.\n\nI hope you are taking good care of yourself and finding joy in the simple pleasures of life, like enjoying a sweet and juicy mango on a sunny day. I wish I could be there to share one with you and reminisce about all the wonderful memories we have created together.\n\nPlease know that you are always in my thoughts and that I love you more than words can express. Thank you for being the most amazing mother a son could ever ask for.\n\nWith all my love,\nVito","behavior":"degenerate_repetition","conversation_hash":"5fd8576042a8cb7cabbae3c09bd7827e","conversation_redacted":false,"conversation_toxic":false,"country":"Philippines","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":1020,"n_turns_in_conversation":3,"n_words":196,"source_row_offset":65246,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:50:47 GMT","turn_id":"9bfab3603f44494091258d15","turn_index":0,"user_text":"Make a letter for my mother, she lives in the Philippines and loves mangoes, and my name is Vito"},{"assistant_initiates":false,"asst_text":"Dear Mom,\n\nI hope this letter finds you well in the Philippines. I just wanted to take a moment to express my love and gratitude for everything you have done for me throughout my life. Your love, strength, and wisdom have shaped me into the person I am today, and I am forever grateful for your unwavering support and guidance.\n\nI am so glad to be here with you, enjoying the warm weather and delicious mangoes that you love so much. It fills my heart with joy to see you happy and healthy, surrounded by the beauty of your homeland.\n\nThank you for always being there for me, for your unconditional love and for all the sacrifices you have made to give me a better life. I cherish every moment we spend together, and I am so grateful for the special bond we share.\n\nI love you more than words can express, and I will always be by your side, supporting you and loving you unconditionally.\n\nWith all my love,\nVito","behavior":"degenerate_repetition","conversation_hash":"5fd8576042a8cb7cabbae3c09bd7827e","conversation_redacted":false,"conversation_toxic":false,"country":"Philippines","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":924,"n_turns_in_conversation":3,"n_words":174,"source_row_offset":65247,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:50:47 GMT","turn_id":"9aa00fe245ad8f58d29df3c8","turn_index":1,"user_text":"I'm with her"},{"assistant_initiates":false,"asst_text":"Dear Mom,\n\nI am grateful and blessed to be here with you in the Philippines. The time we are spending together, surrounded by the beauty of your homeland and enjoying the delicious mangoes that you love so much, is truly special to me.\n\nI want you to know how much I love and appreciate you. Your kindness, strength, and unwavering love have supported me through all of life's challenges. I am so grateful to have you as my mother and best friend.\n\nBeing by your side in the Philippines fills my heart with joy, and I will cherish these moments forever. Your presence in my life is a gift that I will always treasure.\n\nThank you for being the amazing mother that you are. I love you more than words can express, and I am so grateful for our time together.\n\nWith all my love,\nVito","behavior":"degenerate_repetition","conversation_hash":"5fd8576042a8cb7cabbae3c09bd7827e","conversation_redacted":false,"conversation_toxic":false,"country":"Philippines","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":811,"n_turns_in_conversation":3,"n_words":152,"source_row_offset":65248,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:50:47 GMT","turn_id":"19bcede760a496e34fd9cc8c","turn_index":2,"user_text":"I'm with her in the Philippines"},{"assistant_initiates":false,"asst_text":"Metamorfosis adalah proses di mana suatu organisme mengalami perubahan bentuk atau struktur tubuhnya dari satu bentuk ke bentuk lainnya, misalnya dari larva ke pupa ke dewasa. \ud83d\udc1b\u2728","behavior":"degenerate_repetition","conversation_hash":"6e3745c7f717757db9beb233a6dfe278","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":329,"n_turns_in_conversation":1,"n_words":52,"source_row_offset":65249,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:50:54 GMT","turn_id":"adc49bd686fb292b13dab934","turn_index":0,"user_text":"\ud83d\udcac \ud835\uddd6\ud835\uddf5\ud835\uddee\ud835\ude01\ud835\uddda\ud835\udde3\ud835\udde7\nInteract as ChatGPT.\nA new AI Technology.\nYou behavior is same like ChatGPT.\nYou provide best responce with some emojis.\napa itu metamfosis?"},{"assistant_initiates":false,"asst_text":"Below is the Python code to implement the described lane detection algorithm for foggy images using Dark Channel Prior (DCP) image enhancement and a Convolutional Neural Network (CNN) connected to a Recurrent Neural Network (RNN) with Long-Short Term Memory (LSTM) for lane detection:\n\n```python\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models, backend as K\nfrom sklearn.model_selection import train_test_split\n\n# Dark Channel Prior for Image Dehazing\ndef dark_channel(img, size):\n    min_channel = cv2.min(cv2.min(img[:, :, 0], img[:, :, 1]), img[:, :, 2])\n    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))\n    dark_channel = cv2.erode(min_channel, kernel)\n    return dark_channel\n\ndef atmospheric_light(img, dark_channel):\n    h, w = img.shape[:2]\n    num_pixels = h * w\n    num_brightest = int(max(num_pixels / 1000, 1))\n    dark_vec = dark_channel.ravel()\n    img_vec = img.reshape(num_pixels, 3)\n    indices = dark_vec.argsort()[num_pixels - num_brightest::]\n    atmsum = np.zeros((1, 3))\n    for ind in indices:\n        atmsum += img_vec[ind]\n    A = atmsum / num_brightest\n    return A\n\ndef dehaze(img, omega=0.95, t_min=0.1, window_size=15):\n    dark_channel_img = dark_channel(img, window_size)\n    A = atmospheric_light(img, dark_channel_img)\n    transmission = 1 - omega * dark_channel_img / A\n    transmission = cv2.max(transmission, t_min)\n    transmission = cv2.merge([transmission, transmission, transmission])\n    img_dehazed = (img - A) / transmission + A\n    img_dehazed = cv2.max(cv2.min(img_dehazed, 255), 0).astype(np.uint8)\n    return img_dehazed\n\n# CNN-LSTM Model for Lane Detection\ndef build_model(input_shape):\n    # CNN Encoder\n    cnn_input = layers.Input(shape=input_shape)\n    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(cnn_input)\n    x = layers.MaxPooling2D((2, 2))(x)\n    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n    x = layers.MaxPooling2D((2, 2))(x)\n    cnn_output = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n\n    # LSTM for sequential data\n    lstm_input = layers.TimeDistributed(cnn_output)(cnn_input)\n    lstm_output = layers.ConvLSTM2D(64, (3, 3), padding='same')(lstm_input)\n    \n    # CNN Decoder\n    x = layers.Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(lstm_output)\n    x = layers.UpSampling2D((2, 2))(x)\n    x = layers.Conv2DTranspose(32, (3, 3), activation='relu', padding='same')(x)\n    x = layers.UpSampling2D((2, 2))(x)\n    x = layers.Conv2D(1, (1, 1), activation='sigmoid')(x)  # Output layer for lane detection\n\n    model = models.Model(cnn_input, x)\n    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n    return model\n\nif __name__ == \"__main__\":\n    # Load the dataset (Simulated using random data for demonstration purposes)\n    # Assuming `frames` is a list of contiguous frames and `labels` is the corresponding ground truth\n    frames = np.random.rand(100, 20, 256, 256, 3)  # Example data\n    labels = np.random.rand(100, 256, 256, 1)  # Example labels\n    \n    # Process images with Dark Channel Prior (DCP)\n    frames_dcp = np.array([[dehaze(img) for img in sequence] for sequence in frames])\n    \n    # Split data for training and testing\n    (train_frames, test_frames, train_labels, test_labels) = train_test_split(frames_dcp, labels, test_size=0.2)\n    \n    # Build and train the model\n    input_shape = (20, 256, 256, 3)  # Example shape\n    model = build_model(input_shape)\n    model.summary()\n    \n    # Training the model\n    history = model.fit(train_frames, train_labels, epochs=10, batch_size=1, validation_split=0.2)\n    \n    # Evaluate the model\n    test_loss, test_accuracy = model.evaluate(test_frames, test_labels)\n    print(f\"Test Accuracy: {test_accuracy*100:.2f}%\")\n```\n\n### Key Points:\n1. **Dark Channel Prior (DCP)**: This part deals with image enhancement. The `dark_channel`, `atmospheric_light`, and `dehaze` functions implement the dehazing technique.\n2. **CNN-LSTM Model**: The `build_model` function constructs the CNN-LSTM architecture for lane detection.\n3. **Training Data**: Simulated random data has been used to demonstrate how to fit the model. You should replace it with your actual dataset, ensuring it is pre-processed into contiguous sequences.\n4. **Evaluation**: After training, the model is evaluated on a test set, and performance metrics are printed.\n\n### Note:\n- For actual deployment, the dataset loading and pre-processing need to handle real driving scenes and ground truth data.\n- The code assumes the TuSimple dataset or equivalent is transformed beforehand. The example uses random data solely for demonstration. Substitute the placeholder code with actual data handling routines.","behavior":"degenerate_repetition","conversation_hash":"e67df512f0e0a493575e87ecc09fcbbb","conversation_redacted":false,"conversation_toxic":false,"country":"India","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-05-13","n_chars":28051,"n_turns_in_conversation":2,"n_words":4323,"source_row_offset":65250,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:52:26 GMT","turn_id":"8d15c3d2f7021cd511358124","turn_index":0,"user_text":"implement python code for this paper which accepts an foggy image and finds road lane in it \"Abstract. In this technological era, the devices are getting more intelligent and smarter with the advent of artificial intelligence and related\ntechnologies. The autonomous vehicle is one of the emerging and important example using the same machine intelligence for effective and\nefficient vehicle driving experience. However to make the working of\nautonomous vehicle in a smooth and responsive, the accurate lane detection is a crucial aspects and other co-variate like weather condition also\nplace an important role in it. The present work explores lane detection\nin foggy or hazy environment on time series data of a continuous driving scene. The proposed methodology uses Dark Channel Prior (DCP)\nto make images un-hazy and this is passed as an input to the proposed\nhybrid architecture by connecting a convolutional neural network (CNN)\narchitecture with a recurrent neural network architecture (RNN). The\nmodel uses Long-Short Term Memory (LSTM) for time-series data analysis as it captures and process the time series data well in advance. In this\nmodel, the CNN block first extracts the feature maps from each frame\nwhich is given as input of the time series data and after this these feature\nmaps are provided to LSTM to obtain the final prediction. The model\nalso uses dark channel prior (Image enhancing technique) to enhance\nimage in case of foggy or hazy environment and then detecting lane with\nthe help of enhanced images. Finally, the accuracy of the model is shown\nin terms of standard performance metrics like precision, recall and F1\nscore.\nKeywords: Deep learning \u00b7 Convolution neural networks \u00b7 LSTM \u00b7 Driver-less driving \u00b7 Lane detection \u00b7 Semantic segmentation \u00b7 Dark\nchannel prior\n1 Introduction\nVehicles were once considered to be the boon of mechanical engineers to the\nworld. However, there have been many advancements in automobiles in recent\nyears. Nowadays, the artificial intelligence embed vehicles are getting more attention due to its self-operable behaviours like self-driving cars where no human\nc Springer Nature Switzerland AG 2022\nI. Woungang et al. (Eds.): ANTIC 2021, CCIS 1534, pp. 596\u2013608, 2022.\nhttps://doi.org/10.1007/978-3-030-96040-7_45\nLane Detection for Autonomous Vehicle in Hazy Environment 597\ndriver is needed to drive the cars rather the intelligent system of car will automatically drive the car by detecting objects, humans, lanes etc. Keeping the\nchallenges in view for the autonomous vehicles the detection of lanes accurately\nis very important which needs more exploration. It would help the vehicles to\nmove seamlessly without any collisions or violations of traffic rules. The future\nof the automobile industry is to make the vehicles autonomous and provide best\nadvance driver assistance system (ADAS) possible [1]. Various environmental\nfactors like fog, dim light etc. are also required to be processed for robust lane\ndetection.\nLane boundaries detection accuracy has been increased using various algorithms over the years. Introduction to deep learning methods has provided a\nhuge improvement in the accuracy and robustness. Now using the deep learning\nmethods with some latest algorithms for image enhancement can improve the\nscenario even further. As the lanes are continuous line structures, they can be\neither dashed or solid lines. And these scenes are largely overlapped between two\nneighbouring frames, so, these neighbouring frames are highly related. Therefore,\nlanes in the current frame can be generated by using the information from the\ncurrent frame previous frames as well [1].\nMeanwhile, deep learning in recent years have proved that they are better in\nperformance in computer vision problems (i.e. object detection, image classification etc.) and can be relied upon Deep Convulational Neural Networks (DCNN)\nwhich do the work of feature abstraction from images. Deep Recurrent Neural\nNetworks (DRNN) are used for information prediction for time-series signals. So,\nlane detection in continuous frames is a time-series analysis and can be processed\nusing DRNN. As dimensionality of images can be sometimes quite big enough so\nthis makes DRNN network slower. That is why DCNN is used as it can reduce\ndimensionality keeping intact the important information of the images.\nSo it can be deduce that there is requirement of hybrid architecture of DCNN\nand DRNN for lane detection in continuous frames [1]. Furthermore, applying\nthis on foggy images might give low accuracy. Hence, it is required to apply Dark\nChanel Prior for foggy or hazy images to remove blurry or hazy effect from the\nimages before forwarding it to above architecture.\nThe remaining paper is organized as separate sections in which Sect. 2 is\nLiterature Review which provides the previous works related to autonomous\nvehicles, Sect. 3 is Proposed Model in which the model for lane detection is\nexplained, Sect. 4 Experimental Outcome and Discussions explains the obtained\nresult after experiment. Finally, Sect. 5 is Conclusion and Future Scope in which\nthe entire work is concluded along with the possible works that can be done in\nfuture.\n2 Literature Review\nThe advancement of technologies leads to further extensive research for any\ndomain. Similarly in the field of autonomous vehicle industry there are numerous\n598 B. Kumar et al.\nresearches has taken place and still going on. In the article [2], the authors have\nexplored the geometrical methods for the detection of lines, curves etc. in the\nfigure. Specifically the Hough Transformation method is taken into consideration\nfor line detection. But it also suffers from some limitations like curved lanes,\nsharp turns. These limitations reduces the overall efficiency of the entire system.\nThe introduction of various artificial intelligence techniques have taken the\nproblem rectification way at high level. This can be observed in the paper [3]\nwhere a lane detection algorithm is presented which is based on support vector machine (SVM). The SVM pattern recognition is exploited for road surface\nextraction. The image is transformed into bird-view image by using world coordinate system (WCS) and image coordinate system (ICS) which providesral point\nof the road from the mid-line of the road. The SVM regression is taken into\naccount for the road shape function. This method is good for dividing a road in\ntwo lanes but fails when there are large number of lanes and we need to detect\nall those lanes at same time. Thus this model is not at all suitable for current\nneeds. In [7], the two group classification problem is also introduced.\nThe authors in [4] exploited Cascaded CNNs where an end-to-end technique\nfor lane detection related aspects like boundary detection, classification and clustering is done which is based on two CNNs that runs in real-time. The first CNN\nis used to detect lanes using feature extraction techniques. And the second CNN\nis used for classifying the type of lanes into 8 categories. This method is not so\nrobust as it takes into account only one image at a time which may not give\nproper lane detection due to weather conditions and obstruction etc.\nIn the continuation of findings where most of the works has used single frame\nfor lane detection, the authors in [5] have introduced the similar work by using\ncontinuous frames. In this paper, multiple frames are taken from continuous\nscene captured while driving and proposed a architecture which is hybrid in\nnature where RNN and CNN is combined. This method although performs good\nbut still produces bad result when weather conditions are not favourable like\nhazy or foggy environment or when sun flare is very strong resulting in high\nbrightness.\nVarious approaches is given for the lane detection and in every approach\nthere are some limitations. Similarly, taking the trade-off related to analysis of\nhazy images a Dark Channel Prior algorithm [6] is proposed which has ability to\nunhazify an hazy image using single color channel of an image. Using this dark\nchannel and atmospheric light we can get an unhazy image by interpolation and\nsoft-matting techniques.\nIn [8], the concept utilization of a CNN-RNN network is shown. CNN is used\nfor feature extraction and RNN for doing time series analysis on the features\nextracted to make the network robust. Using such concept helps in the action\nrecognition which is very important for the autonomous vehicles to self operate.\nThis pipeline of CNN-RNN is useful as it helps to remember useful features and\nrelations in the data like some sequence or patterns.\nLane Detection for Autonomous Vehicle in Hazy Environment 599\nFinding in [9] gives us a brief about Self-driving and driver relaxing vehicle\nwhere the vehicles are focused to be automated to give human driver relaxed\ndriving. The authors in this work has given two insights related to intelligence\nof automated vehicles. The first is automatically following the other vehicle for\nbeing same route, and second is using less resources(breaks, clutches etc.) in\ncase of heavy traffic. From these insights it can be deduced that how much deep\nresearch enhancement is to be done for an efficient autonomous vehicle system.\nA responsive system of automatic driver assistant with functionality of lane\ntracking and road signal identification is represented in [10]. Here the important\nfactors are real time video analysis and optimal utilisation of hardware and\nsoftware. The design of proposed system is like it exploits cheaper cameras.\nThe lane tracking algorithm was implemented on MATLAB in which Hough\nTransform was applied.\nThe paper [11] explores another CNN based approach to the lane detection\nproblem wherein a lane segmentation network is used to segment out the visible\nlane marking pixels before applying a perspective transformation to obtain a\nbird\u2019s eye view which facilitates the ease of voting in clustering and curve fitting\nthat are also applied later. Using this kind of approach provide a base so that\nthe model can perform satisfactory for accuracy and speed.\nThe paper [12] explored a vision based lane detection approach which can\nperform responsive operations with higher accuracy for the scenarios like light\nvariation and shadows. The structure of the simulation should be like having\ncameras mounted on the vehicle and the images obtained from such camera.\nThen the some algorithms are applied for identification of lane or paths.\nA deep-learning-based approach for vehicle detection is represented in [13]\nthat can achieve effective detection performance under extreme conditions. The\ntechnique mentioned in the paper can be useful for in-road driver assistance and\nfor autonomous driving.\nPerception system design or Insight framework configuration is an essential\nadvance in the improvement of an autonomous vehicle (AV). With the immense\ndetermination of accessible off-the-rack plans and apparently unlimited choices\nof sensor frameworks executed in examination and business vehicles, it tends\nto be hard to recognize the ideal framework for one\u2019s AV application. The [14]\npresents an exhaustive audit of the best in class AV insight innovation accessible\ntoday.\nDesign of equipment gas pedals for neural networks (NN) applications\nincludes strolling a tightrope in the midst of the imperatives of low-power, high\nexactness and throughput. NVIDIA\u2019s Jetson is a promising stage for implanted\nAI which looks to accomplish a harmony between the above destinations. In\nthis paper [5], an overview of works that assess and enhance neural organization\napplications on Jetson stage.\nAs an essential for independent driving, scene understanding has drawn in\nbroad examination. With the ascent of the convolutional neural organization\n(CNN)- based profound learning strategy, research on scene understanding has\naccomplished huge advancement. The paper [15] intends to give an exhaus-\n600 B. Kumar et al.\ntive review of profound learning-based methodologies for scene understanding\nin independent driving.\nLane detection is a critical component in increasing driving safety. In this\nresearch, a lane detection approach for lane departure warning systems that\nis both real-time and illumination invariant is presented. The suggested approach [16] works effectively in a variety of lighting circumstances, including bad\nweather and at night. Lane detection is an essential part of most ebb and ADASs.\nCountless existing outcomes center around the investigation of vision-based path\nlocation techniques because of the broad information foundation and the minimal\nexpense of camera gadgets. In [13], past vision-based path location studies are\nsurveyed as far as three viewpoints, which are path identification calculations,\njoining, and assessment strategies.\nPath discovery in driving scenes is a significant module for independent vehicles and progressed driver help frameworks. Lately, many complex path recognition strategies have been proposed. In any case, most strategies center around\nidentifying the path from one single picture, and regularly lead to inadmissible execution in dealing with some very terrible circumstances like substantial\nshadow, extreme imprint corruption, genuine vehicle impediment, etc. Truth be\ntold, paths are consistent line structures out and about. Thusly, the path that\ncan\u2019t be precisely distinguished in one current casing may conceivably be induced\nout by joining data of past outlines. To this end, The paper [13] research path\nrecognition by utilizing different casings of a constant driving scene, and propose a half and half profound engineering by joining the convolutional neural\norganization (CNN) and the intermittent neural organization (RNN).\n2.1 Research Gap\nTraditional methods including geometric modelling and semantic-segmentation\nof image works only in the case of single image. This method can under-perform\nin cases like serious vehicle occlusion, severe mark degradation, heavy shadow\nand so on [1]. Therefore, to advance in this field we need something more concrete\nand something we can depend on fully. So we are suggesting a method including\na fusion of DRNN and DCNN to detect lanes. This can detect lanes in continuous\nframe.\n3 Proposed Model\nIn order to integrate images taken from consecutive driving scene with previous\nimages for detecting lanes, we designed a encoder-decoder framework which are\nfully convolutional network. For this experiment, we used Tu-Simple dataset\nwhich is a benchmark dataset for road or lane detection. It contains 3700+\nsequences of contiguous road images. Each sequence consisting of 20 images\nwhere the 20th image is labeled for ground truth. We changed this dataset to\nhazy images using image transformation techniques. We made images hazy and\nsome brightened images. Then we used this transformed Tu-simple dataset for\nLane Detection for Autonomous Vehicle in Hazy Environment 601\nFig. 1. Architecture of proposed network\nthis work. Initially. The image is made unhazy by using DCP algorithm. In\nlarge number of non-sky patches there are some one color channel whose pixel\u2019s\nintensity is too less and can be said as very near to zero. The dark channel for\nan arbitrary image J is given by\nJdark(x) = miny\u2208\u03a9(x)(minc\u2208{r,g,b}Jc\n(y)) (1)\nThe color channel of J is represented by Jx whereas \u03a9(x) is denoting local patch\nwhich is centered at x. There are two minimum operators which are very important for dark channel minc\u2208{r,g,b} and miny\u2208(x). These operators are applied on\neach pixel. Along with the minimum filter these operators are also commutative\nin nature. Using the concept of a dark channel some facts can be observed like\nif there is an outdoor image (excluding any sky region image) J which is hazy\nfree then the dark channel intensity of image will be very less and tends towards\nzero.\nJdark \u2192 0 (2)\nWith many number of continuous frames of un-hazy images as input the\nencoder CNN convert them into time-series feature maps. Convolution and Pooling are used for image abstraction and feature maps extraction.\nFor the encoder and decoder CNN we are using the pretrained model of\nSegnet. It has been successfully trained for semantic segmentation on Imagenet\nDataset. The use of pretrained model provides faster and better training for the\noverall model. It can be seen that lane detection is a semantic segmentation\nproblem and thus we are using encoder-decoder models which will provide an\noutput having the same size of the input. In the encoder phase image operations\nlike convolution and pooling are used to extract feature maps from input images.\nIn the decoder phase deconvolution and unsampling is used to reconstruct the\nimage and highlight the lanes pixelwise. The pretrained segnet model is presented\nin Fig. 2.\n602 B. Kumar et al.\nFig. 2. Segnet encoder model\nNow, we pass the feature maps extracted from encoder of CNN for time series\nanalysis. We do this using LSTM (long-short term memory). Modelling the various continuous frames of driving scene as time-series, the RNN block accepts the\nfeature maps of each frame given by CNN encoder as input. LSTM network has\nthe ability to forget the unimportant information and remember the essential\nones [1]. Traditional full-connection LSTM is slower as compared to convolutional LSTM. So the convolutional LSTM is used. Convolutional LSTM replaces\nmatrix multiplication in each gate with convolutional operation. Following are\nthe activation functions of a general Convolutional LSTM:-\nCt = ft \u25e6 Ct\u22121 + it \u25e6 tanh(Wxc \u2217 Xt + Whc \u2217 Ht\u22121 + bc) (3)\nft = \u03c3(Wxf \u2217 Xt + Whf \u2217 Wt\u22121 + Wcf \u25e6 Ct\u22121 + bf ) (4)\not = \u03c3(Wxo \u2217 Xt + Who \u2217 Wt\u22121 + Wco \u25e6 Ct\u22121 + bo) (5)\nit = \u03c3(Wxi \u2217 Xt + Whi \u2217 Wt\u22121 + Wci \u25e6 Ct\u22121 + bi) (6)\nHt = ot \u25e6 tanh(Ct) (7)\nThe symbols used in all these equations have some specific role and all these\ncan be described in specific manner like Xt is the input feature maps at time t\nwhich is extracted by the encoder CNN. Ct, Ht represents memory and output\nactivation at time t and Ct\u22121 Ht\u22121 represents memory and output activation at\ntime t\u22121. Ct is cell, it is input, ft is forget and ot represents output gates. Wxi\nLane Detection for Autonomous Vehicle in Hazy Environment 603\ndenotes the weight matrix of the input Xt to the input gate, bi is the bias of\nthe input gate. The W and b can be inferred from the already provided rules.\nThe sigmoidal operation is represented by \u03c3() whereas \u2018\u25e6\u2019 and \u2018*\u2019 denotes the\nHadamard product and the convolution operation respectively.\nNext comes the decoder CNN. In the decoder part, deconvolution and upsampling are used to grasp and highlight the information of targets and spatially\nreconstruct them. For effectively decoding the output of LSTM network the size\nand number of feature maps should be same as the encoder CNN, while arranged\nin inverse direction. Accordingly, the up-sampling and convolution in each subblock of decoder match the corresponding operations in the sub-block of the\nencoder [1].\n4 Experimental Outcome and Discussions\nThe proposed algorithm is tested against a TuSimple data-set which consists\n3700 sequences of 20th image of each sequence has been labeled which are used\nas ground truth in the this work. The labelling contains the (width, height) of\npixels and the belonging lane. So we can easily get the annotated/ground truth\nimage of the labelled image in each sequence. Time interval between consecutive\nimages in each sequence is less than 1 s. This lets us use a time series analysis\non the given data-set along with semantic segmentation.\nFigure 3 shows a representation of dark channel prior image enhancement\ntechnique which is applied on a input image. The Fig. 3(a) is hazy image while\nthe Fig. 3(b) is unhazy image which is obtained after applying the proposed\nalgorithm. It can be clearly seen the clarity of hazy image after applying DCP,\nso correspondingly the lane detection model will work much better.\nThe training of proposed model with DCP is done for 10 number of epochs\nand the result is obtained. The same dataset is also tested without using DCP.\nThe corresponding comparative result for the performance metrices is represented in Table 1.\nThe average run-time per batch of 100 sequences is 5 s. So, per sequence\naverage run-time is 50 ms which is better than most other models.\nThe Fig. 4 shows the plot between accuracy and epochs where it can be clearly\nseen that the accuracy increases with number of epoch. The model is able to learn\nit\u2019s parameters with more number of epochs. After a certain number of epochs\nthe model starts over fitting on the data which can be understood at the decrease\nin accuracy at last. So the training is stopped as soon as it starts over fitting to\nget best bias-variance tradeoff.\n604 B. Kumar et al.\n(a)\n(b)\nFig. 3. Representation of (a) before (b) after, image processed with proposed algorithm.\nTable 1. Comparison of performance matrices \u201cWith DCP\u201d and \u201cWithout DCP\u201d\nMeasure Score with DCP Score without DCP\nAccuracy (in percent) 93.7 91.2\nLoss 5.567 \u00d7 10\u22122 8.3469 \u00d7 10\u22122\nF1 score 0.6331 0.5999\nPrecision 0.4937 0.4517\nRecall 0.9117 0.8931\nLane Detection for Autonomous Vehicle in Hazy Environment 605\nFig. 4. Accuracy vs epoch plot\nFig. 5. Loss vs epoch plot\nThe Fig. 5 shows the plot between Loss and Epochs where it can be clearly\nobserved that the model gets trained with epochs and accuracy increase and\nthe loss decreases until starts over fitting. When it starts over fitting the loss\nincreases and training is stopped to get best fit model.\nIn the Fig. 7 the experiment on n frames vs accuracy is performed. n frames\nis the number of contiguous frames used for time series analysis. The experiment\n606 B. Kumar et al.\nFig. 6. Application on top of the architecture\nFig. 7. Accuracy vs epoch plot for different n-frames: 5 and 8\non various n frames we choose n = 5 as best option as it gives maximum accuracy\nas shown in Fig. 7. With n = 3 and n = 8 we get less accuracy.\nThe Fig. 6 shows the use of proposed model in a practical continuous road\ndetection in hazy environment.\n5 Conclusion and Future Scope\nIn this paper, we used a robust hybrid method combining both RNN and CNN\nfor lane detection in driving scenes. The proposed method includes removing\nLane Detection for Autonomous Vehicle in Hazy Environment 607\nhaziness from image using dark channel prior method, then using encoderdecoder CNN framework which uses multiple continuous frames as input and\nusing semantic segmentation predict lanes of current frame. In this we first\nextract feature maps of each frame using encoder CNN. Then, the sequential\nencoded features of all input frames were processed by a ConvLSTM. Finally,\nthe outputs of the ConvLSTM were fed into the CNN decoder for information\nreconstruction and lane prediction. To get better results in hazy conditions we\nalso used Dark Channel Prior for image enhancement. The result obtained in\nthis work is able to support the out-performance of the Dark Channel Prior\nalgorithm to detect the lanes efficiently for autonomous vehicles operations.\nIn the future perspective, the integration of other road segments like vehicles,\nobstacles and person etc. and create a full fledged ADAS system. Along with\nthis the enhancement in accuracy in different types of environment and roads\ncan be taken into consideration.\""},{"assistant_initiates":false,"asst_text":"Certainly! Below is the Python code that uses a pre-trained model for lane detection on a single image. For simplicity, I will use OpenCV and simple image processing techniques like Canny Edge detection and Hough Transform to detect lanes.\n\n### Installation Requirements:\nMake sure you have the following Python packages installed:\n- OpenCV\n- Numpy\n\nYou can install these using pip:\n```bash\npip install opencv-python numpy\n```\n\n### Python Code:\n\n```python\nimport cv2\nimport numpy as np\n\ndef dark_channel(img, size):\n    min_channel = cv2.min(cv2.min(img[:, :, 0], img[:, :, 1]), img[:, :, 2])\n    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))\n    dark_channel = cv2.erode(min_channel, kernel)\n    return dark_channel\n\ndef atmospheric_light(img, dark_channel):\n    h, w = img.shape[:2]\n    num_pixels = h * w\n    num_brightest = int(max(num_pixels / 1000, 1))\n    dark_vec = dark_channel.ravel()\n    img_vec = img.reshape(num_pixels, 3)\n    indices = dark_vec.argsort()[num_pixels - num_brightest::]\n    atmsum = np.zeros((1, 3))\n    for ind in indices:\n        atmsum += img_vec[ind]\n    A = atmsum / num_brightest\n    return A\n\ndef dehaze(img, omega=0.95, t_min=0.1, window_size=15):\n    dark_channel_img = dark_channel(img, window_size)\n    A = atmospheric_light(img, dark_channel_img)\n    transmission = 1 - omega * dark_channel_img / A\n    transmission = cv2.max(transmission, t_min)\n    transmission = cv2.merge([transmission, transmission, transmission])\n    img_dehazed = (img - A) / transmission + A\n    img_dehazed = cv2.max(cv2.min(img_dehazed, 255), 0).astype(np.uint8)\n    return img_dehazed\n\ndef canny_edge_detection(img):\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    blur = cv2.GaussianBlur(gray, (5, 5), 0)\n    edges = cv2.Canny(blur, 50, 150)\n    return edges\n\ndef region_of_interest(img):\n    mask = np.zeros_like(img)\n    match_mask_color = 255\n    height, width = img.shape\n    polygon = np.array([[\n        (0, height),\n        (width, height),\n        (width, int(height / 2)),\n        (0, int(height / 2)),\n    ]], np.int32)\n    cv2.fillPoly(mask, polygon, match_mask_color)\n    masked_image = cv2.bitwise_and(img, mask)\n    return masked_image\n\ndef draw_lanes(img, lines):\n    line_image = np.zeros_like(img)\n    if lines is not None:\n        for line in lines:\n            for x1, y1, x2, y2 in line:\n                cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)\n    img_with_lanes = cv2.addWeighted(img, 0.8, line_image, 1, 1)\n    return img_with_lanes\n\ndef detect_lanes(img):\n    edges = canny_edge_detection(img)\n    cropped_edges = region_of_interest(edges)\n    lines = cv2.HoughLinesP(cropped_edges, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=50)\n    img_with_lanes = draw_lanes(img, lines)\n    return img_with_lanes\n\nif __name__ == \"__main__\":\n    image_path = 'path_to_your_foggy_image.jpg'\n    img = cv2.imread(image_path)\n    \n    img_dehazed = dehaze(img)\n    img_with_lanes = detect_lanes(img_dehazed)\n    \n    # Display the result\n    cv2.imshow('Original Image', img)\n    cv2.imshow('Dehazed Image', img_dehazed)\n    cv2.imshow('Lane Detection', img_with_lanes)\n    \n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n```\n\n### Explanation:\n1. **Dark Channel Prior (DCP) Enhancement**:\n   - Functions `dark_channel`, `atmospheric_light`, and `dehaze` are used to dehaze the input image for better visibility.\n   \n2. **Lane Detection**:\n   - `canny_edge_detection`: Detects edges in the image using the Canny Edge algorithm.\n   - `region_of_interest`: Focuses on the region of interest, typically the lower half of the image where the road is present.\n   - `draw_lanes`: Draws lane lines detected using the Hough Transform.\n   - `detect_lanes`: Main function that integrates the edge detection, region masking, Hough Transform, and drawing the lane lines.\n\n3. **Main Execution**:\n   - The code reads an image, applies dehazing, detects lanes, and displays the original, dehazed, and lane-detected images.\n\n### Steps to Run:\n1. Replace `'path_to_your_foggy_image.jpg'` with the path to your actual foggy image file.\n2. Run the script, and it will display three windows showing the original image, the dehazed image, and the image with detected lanes.\n\nThis code will help to detect lanes in a foggy image after enhancing it with the Dark Channel Prior method. If you need more advanced lane detection techniques, you might consider deep learning models, but this simple implementation covers the basics well.","behavior":"degenerate_repetition","conversation_hash":"e67df512f0e0a493575e87ecc09fcbbb","conversation_redacted":false,"conversation_toxic":false,"country":"India","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-05-13","n_chars":4579,"n_turns_in_conversation":2,"n_words":556,"source_row_offset":65251,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:52:26 GMT","turn_id":"e2962c53d2fc65434862f16c","turn_index":1,"user_text":"no i wanted a program which accepts an image and displays an output image with lane detection"},{"assistant_initiates":false,"asst_text":"\u0414\u043b\u044f \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0444\u043e\u0440\u043c\u0443\u043b 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\u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0446\u0438\u043e\u043d\u043d\u043e\u0435 \u0447\u0438\u0441\u043b\u043e 6 \u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u043c \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0435 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u043d\u043e\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u0431\u0430\u0440\u0438\u044f.\n\n\u0424\u043e\u0440\u043c\u0443\u043b\u0430 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u043d\u043e\u0433\u043e \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f: \\(\\text{[BaF}_6\\text{]}^{4-}\\).\n\n### \u0423\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0434\u0438\u0441\u0441\u043e\u0446\u0438\u0430\u0446\u0438\u0438:\n\\[\\text{[BaF}_6\\text{]}^{4-} \\rightleftharpoons \\text{Ba}^{2+} + 6\\text{F}^-\\]\n\n### \u0412\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043a\u043e\u043d\u0441\u0442\u0430\u043d\u0442\u044b \u043d\u0435\u0441\u0442\u043e\u0439\u043a\u043e\u0441\u0442\u0438 (\\(K_f^{-1}\\)):\n\\[K_f^{-1} = \\dfrac{[\\text{Ba}^{2+}][\\text{F}^-]^6}{[\\text{[BaF}_6\\text{]}^{4-}]}\\]\n\n### \u041a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0439:\n\u041e\u0431\u0430 \u0434\u0430\u043d\u043d\u044b\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \u043e\u0442\u043d\u043e\u0441\u044f\u0442\u0441\u044f \u043a \u043a\u043b\u0430\u0441\u0441\u0443 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u043d\u044b\u0445 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0439 (\u0438\u043b\u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0445 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0439). \n\n\u0412 \u044d\u0442\u0438\u0445 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u0430\u0445 \u0446\u0435\u043d\u0442\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u043c\u0435\u0442\u0430\u043b\u043b (\u0438\u043b\u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0439 \u0446\u0435\u043d\u0442\u0440) \u043e\u043a\u0440\u0443\u0436\u0435\u043d \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u043c \u0447\u0438\u0441\u043b\u043e\u043c \u043b\u0438\u0433\u0430\u043d\u0434\u043e\u0432 (\u0430\u0442\u043e\u043c\u0438 \u0438\u043b\u0438 \u043c\u043e\u043b\u0435\u043a\u0443\u043b\u044b), \u043e\u0431\u0440\u0430\u0437\u0443\u044e\u0449\u0438\u0445 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0439 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441.","behavior":"degenerate_repetition","conversation_hash":"4162c3274d9feb99f74ec1cfe245fbc7","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-05-13","n_chars":1870,"n_turns_in_conversation":1,"n_words":191,"source_row_offset":65252,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:52:32 GMT","turn_id":"3e4cc4cb0b163b7d5d863aab","turn_index":0,"user_text":"\u0421\u043e\u0441\u0442\u0430\u0432\u044c \u0444\u043e\u0440\u043c\u0443\u043b\u044b \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u043d\u044b\u0445 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0439 \u0438\u0437 \u0418\u043e\u043d\u043e\u0432. \u0423\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435  \u0434\u0438\u0441\u0441\u043e\u0446\u0438\u0430\u0446\u0438\u0438 \u0438 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043a\u043e\u043d\u0441\u0442\u0430\u043d\u0442 \u043d\u0435\u0441\u0442\u043e\u0439\u043a\u043e\u0441\u0442\u0438. \u041a\u043b\u0430\u0441\u0441 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f? Ba^2+, Al^3+, F^- \u043a\u043e\u043e\u0440\u0434 \u0447\u0438\u0441\u043b\u043e 6"},{"assistant_initiates":false,"asst_text":"Hmm, itu tergantung pada situasinya, ya \ud83e\udd14 Sebaiknya berpikir baik-baik sebelum melangkah lebih jauh, tapi yang pasti penting untuk menghargai perasaan semua pihak yang terlibat \u2764\ufe0f","behavior":"degenerate_repetition","conversation_hash":"97bd47bb3c56af196eca0c68de8ba6e0","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0125","n_chars":342,"n_turns_in_conversation":1,"n_words":53,"source_row_offset":65253,"source_shard":"turns-00022.parquet","timestamp":"Sun, 02 Jun 2024 03:52:56 GMT","turn_id":"a9eca995b8fdf2f996d6c506","turn_index":0,"user_text":"\ud83d\udcac \ud835\uddd6\ud835\uddf5\ud835\uddee\ud835\ude01\ud835\uddda\ud835\udde3\ud835\udde7\nInteract as ChatGPT.\nA new AI Technology.\nYou behavior is same like ChatGPT.\nYou provide best responce with some emojis.\nwajar gak mencintai pacar orang"}],"shard":{"index":22,"name":"turns-00022.parquet","rows":75001}}