dataesr/scientific-paragraphs-categorization huggingface dataset:
As described in that paper, the code was verified against observations of lenses by the galaxy-scale search of 2 https://github. com/tcollett/LensPop the Canada-France-Hawaii Telescope Legacy Survey  (More et al. 2012 (More et al. , 2016;; Gavazzi et al. 2014). The paper predicted lenses discoverable by the Wide Field Survey of Euclid  (Laureijs et al. 2011) , the Dark Energy Survey  (Treu et al. 2018)  and the Rubin Observatory  (Ivezic et al. 2008). The model was used in  Weiner (2019)  to predict 122 strong galaxy-galaxy lenses in COSMOS, in broad agreement with the 23 highly likely lenses and 159 lens candidates in that field  (Faure et al. 2008; Jackson 2008) , to make predictions for the Euclid Deep Fields and to examine dependencies on cosmological parameters. A further prediction of ≥17 000 lenses discoverable by the Nancy Roman Space Telescope  (Green et al.
CONCLUSIONS We present spectroscopic redshifts for a sample of the brightest (S500 > 0. 08 Jy) sources from the equatorial and Southern fields of the H-ATLAS Survey using data taken with the ALMA ACA and the full 12-m Array. From these observations, 71 robust redshift measurements were obtained from emission line detection using ALMA at a high efficiency (73 per cent of sources). Our results can be further summarised as follows. (i) Combining our results, we find that our sources lie within the redshift range 1. 4 < z < 4. 5 and, where available for comparison, all lie at redshifts greater than the likely foreground lens redshift. (ii) The distribution of CO emission line widths was found to be 100 km s -1 < FWHM < 1290 km s -1 , with a mean value of 494 km s -1 , suggestive of complex dynamics.
(iii) The observed magnification distribution is consistent with galaxy-galaxy strong lensing models at magnifications < 10, but there are hints of an excess at higher magnifications. This could be due to an additional contribution to the strong gravitational lensing optical depth in wide, blank-field surveys from galaxy groups and clusters as found in SMG magnification bias studies. of the project Z-GAL ANR-AAPG2019 of the French National Research Agency (ANR). HD acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities (MICIU) under the 2014 Ramón y Cajal program RYC-2014-15686, from the Agencia Estatal de Investigación del Ministerio de Ciencia e Innovación (AEI-MCINN) under grant (La evolución de los cíumulos de galaxias desde el amanecer hasta el mediodía cósmico) with reference (PID2019-105776GB-I00/DOI:10.
13039/501100011033) and acknowledge support from the ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de Canarias and the European Regional Development Fund (ERDF) under grant with reference PROID2020010107. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2016. 2. 00133. S, 2018. 1. 00804. S, and 2019. 1. 0147. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. This research made use of Astropy, 3 a communitydeveloped core Python package for Astronomy (Astropy  Collaboration et al. 2013 Collaboration et al. , 2018)). Data analysis made use of the Python package Numpy  (Harris et al.
ACKNOWLEDGEMENTS Herschel is an  ESA  space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from  NASA. SS was partly supported by the  ESCAPE  project; ESCAPE -The  European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement no. 824064. SS also thanks the  Science and Technology Facilities Council  for financial support under grant  ST/P000584/1. SU would like to thank the  Open University School of Physical Sciences  for supporting this work. We thank  Z. -Y. Cai  for kind assistance with the submm population models. RG thanks  Churchill College in Cambridge  for its hospitality and support.
TB and YT acknowledge funding from  NAOJ ALMA   Scientific Research Grant Numbers   2018-09B  and  JSPS KAKENHI  No. 17H06130. KK acknowledges the support by  JSPS KAKENHI  Grant Number  17H06130. CY acknowledges support from an  ESO  Fellowship. RI is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy-EXC-2094-390783311. JGN acknowledges the PGC 2018 project  PGC2018-101948-B-100  ( MICINN/FEDER). MM acknowledges the support from grant  PRIN MIUR 2017 -20173ML3WW 001. This work benefited from the support.
DATA AVAILABILITY The Herschel SPIRE data can be downloaded at https://www. h-atlas. org , while reduced and calibrated science-ready ALMA data is available on the ALMA Science Archive at https://almascience. eso. org/asax/. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2016ALMA#. 2. 00133. S, 2018ALMA#. 1. 00804. S, and  2019. 1. 01477. S. 1. 01477. S.
In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.
The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture.
, 2018). The related video retrieval denotes the work of retrieving the same video gathering, by the user input query to the system. Multimedia resources get wide benefits from vigorous video retrieval schemes such as news channel analysis of videos, broadcasting of desired videos, analysis of marketable videos, modern museum-like digital, and action of surveillance in videos. In the previous years, the collections of videos small and video retrieve were placed manual annotation. The current high growth of digital data is increasing day by day, due to this increase the advanced technology will be supported in multimedia schemes. Manual annotation has been no more reliable in the retrieval of videos. As a result, it makes a great request for automatic video retrieval systems. User input query and retrieve video from the database using a specific model query.
Convolutional Neural Network design provide a correct statement on behalf of stating inputs digital data and call the network to compile these properties to the design. A reduce the number of parameters to the next function makes that more efficient to implement the network  (Iqbal et al. , 2018). In the current era of information technology, digital data is played a vital role in law enforcement, office work, and entertainment, and everywhere in life. The internet and offline systems are big sources of videos. On the internet and offline databases have big data and all users access these data through an index. All the mention resources use indexing retrieval. Somehow in Google using content-based image retrieval but does not have video retrieval. Formulation of the query annotation is generally the automation of content instead of indexing annotation.
Motivation and Background The primary use of the system is to retrieve similar videos that are present in the database, such as retrieving the whole movie from its trailer. And this system can also use for database management, just search a video in the database and store it in categories wise. But once the system is implemented it can be used for diversified applications just by changing the algorithms. For example, if we change the algorithm to object detection. There are some other applications of the content-based video retrieval system. Searching videos in a large database is a very useful application in this environment because through the text they may retrieve fake names videos. From news, the archive retrieves news on demand. This research can also use for security purposes to detect a specific event.
In education searching lecture related data. In the shopping mall, we can use for product information. Searching the desired video in a big database by text may have a fake name or wrong name so you will be retrieved undesired video. Hence content-based video retrieval can use in any digital environment. Resourceful video retrieval has been applied earlier on the videos but to the best of our knowledge, it was neither efficient nor accurate. It has some limitations and the first one is that it was taking a long time i. e. , it has the highest time complexity at the time of process when video data was being called by their contents. The existing work uses some approaches for the sake of improvements of the CBVR system. At the early stage, the video query was obtained. Then the feature extraction was taken into account.
Then the query of the target file of the video was forwarded for a similar matching of the contents based on keyframes and some identical terms. On the other hand, the video collection and then the extraction were taken into account. These all were then checked into their created database and that database was linked directly with the similarity matching and at the end, retrieved videos were to be obtained as requested by the earlier video at the time of searching into the database. Unlike the existing work, the proposed work possesses the advantage of using an efficient video extraction system. The main difference between the existing work and the proposed work is that the proposed work takes less time at the time of processing the video retrieval and gives an efficient and robust result which is remarkably an outstanding performance as compared to the existing work.
Zhang et al. (2019)  authors suppose that to solve the problem of large-scale video retrieval by an image query. They were given a top k image video query. After that, they combined the CNN for short and BoVW for short modules to construct a system for video frame information extraction and representation. For a large scale, video retrieval needed they proposed a visual weight inverted index and algorithm for the improvement of performance and accuracy of the process. And the suggested system got an improvement in the state of the approaches with better accuracy. They found the top k image for query and retrieved the top most related videos from the database. And experimentally they achieved good result from the state of the art methods with respect to accuracy. Bolettieri et al. (2019)   of retrieval.
In HDPS some structures of video summarization have not been measured. Thus, they proposed the VRHDPS clustering algorithm, which was more reliable for video summarization. Ouadrhiri et al. (  2017 ) suggested using spatial-temporal types to describe videos. Bounded Coordinate of Motion Histogram was acquainting to describe and match video subsequences in a reduced calculation interval. Furthermore, the suggested method was adaptive: presented a training procedure. And on a database of 1707 movie clips the cost was expressed in comparison measurement has its accuracy improved by more than double (approximately 38%) in association with extended fast dynamic time distorting method. the challenge was, to make the related signatures built on gesture and, and other was a very fast similarity measure must, therefore, be used to matched video subsequences.
Straight forward rules were used for keyframes multiple feature extractions. They used the Euclidean distance formula to measure the values in feature vector and query. The CBVR systems were compared with the proposed system with a single feature. After the experiment shows that from the single feature system the multiple feature system performs better. Iqbal et al. (2018)  used the digital image processing method (Eigenface, the histogram of gradients, active appearance model, and Haar features) on Query Process Model that retrieved a list of videos from a database. And clustering method (k mean, SVM, and K-Nearest Neighbor) and consequence (testing and training) are with their confusion matrix (specificity and Sensitivity). As compared to convolutional Neural Network result with other clustering techniques the CNN was outstanding.
Song et al. (2018)  proposed a framework named Self Supervised Video Hashing (SSVH). That was able in hash fashion manner to capture the time base nature of the videos in the end to end learning. Two main problems specifically they addressed: 1) how to design converter and reconverted system to generate for video a binary code; and 2) how for accurate video retrieval train the binary codes with the ability. FCVID and YFC are two data sets on which the results were tested. And show that has SSVH approach has suggestively outclassed the state of the art systems and achieve the presently best result on the task. Tarigan et al. (2018)  invented an implementation of CBVR using SURF. In query give an image, the system search videos in database and retrieve similar videos with query image that matched with video frames.
By the observation, the proposed framework was motivated that RPCA enters data into (1) the features of the dataset a low-position segment that uncovers the methodical data over the framework, and (2), in the equivalent dataset a lot of inadequate parts every one of which contains unmistakable data. The two data types were consolidated into a solitary '1-standard based non-arched advancement issue to recover the needed number of keyframes. Besides, the answer to the advancement issue they were plans another iterative calculation. The proposed RPCA and Ijaz Ullah based system doesn't require shot(s) division, semantic comprehension, or acknowledgment of the key film. At long last, on the client video, the tests were performed. A correlation of the outcomes acquired by has a strategy with related best in class and the ground truth calculations obviously show the attainability of the proposed RPCA based structure.
In this research we talk about Video key frames, indexing, extract CNN features through deep learning used for video indexing, browsing, and retrieval. Our proposed CBVR (Content-Based Video Retrieval) system, architecture shows a novel procedure for similar information retrieval. Our proposed framework includes multiple modules or steps. In the first step, we read the video and convert it into multiple frames, in the next step we are extracted handcrafted features from all frames and select key-frames through unsupervised learning, clustering technique. After selection of keyframes, we extract CNN (ConvNets) features using deep learning from key-frames and save that features in a single feature vector, the above process is used for query video as well as for videos database, at the end we find the difference between query video and database through distance measurement equation (distance matrix) to calculate the distance.
Euclidian Distance-Based Similarity Euclidean distance method is used for the difference between the input query and database query. The Euclidian equation subtracts one query from another through feature vector and measures the difference, which is mention below. Where the features are extracted from the input query and database query, in which the first parameter v is the input query vector and the second one database query vector. Distance 'd' is calculated for query videos with database query videos. The greater will be occurring when the d is smaller. When the d values become smaller, the result will be good between two (query video and database video) and hence the results will be greater when claimed. Where features are extracted from query video as well as dataset video and find the most similar video are retrieved from the database, which is near to query video contents and follow the most well-known equation (Euclidean distance measurement) that is most efficient distance measurement equation, which is proved through experimental results  (Iqbal et al.
Precision and Recall Our system is evaluated on two metrics and recall. The most popular evaluated methods, these two techniques are used for the content-based video retrieval system. Precision P is calculated for the number of same elements retrieved and the number of elements retrieved from the database; it calculates the accuracy of the retrieval system. Recall R is calculated between the two values. The ratio between the number of correct elements retrieved and the total number of related elements that are presented in the database. Precision P and recall R are calculated by the following equations.
Mean square error Mean square error (MSE) is a metric that fined the difference between the predicted values from the observed values between 0 and 1 in the analysis. The symbol of sigma in mathematics, the character that looks like E is called summation. That is the summation of all values, from start i=1 till n, through this we add all points from start to an end. For each point, we take the y is the predicted points for the ith observations, and the 'y' is the correct observed values for the ith observation. We subtract the correct observed values from the 'y' predicted values and calculate the square of the result. In the last to put the summation of all the (y -y')² values, and divide it by n, n is the total number of videos in the database which will give the mean square error  (Mathieu et al.
Dataset We are using UCF101; this data set in the current time is a huge data set of human actions. They contain 101 classes of actions and 13k short clips and 27 hours of video data. This data set contains user-uploaded video and cluttered backgrounds. Moreover, this data set provides standard activity acknowledgment results on this new dataset utilizing the standard sack of words approach with the general execution of 44. 5%. As far as we could possibly know, UCF101 is at present the most testing and Ijaz Ullah In this section, a thorough overview of the proposed model has been given along with the simulation tool and simulation performance parameters for evaluation and analysis. This section has given a complete idea that how the proposed work has been carried out by using the proposed simulation environment and parameters.
Dataset We are using UCF101; in which data is organized in 101 groups, And 13320 videos from 101 categories, which is as of now the biggest dataset of human activities. It comprises 101 activity classes, this informational index comprises over 13k clasps and 27 hours of video information. The database contains sensible client transferred recordings containing camera movement and a jumbled foundation. Moreover, this informational index gives gauge activity acknowledgment results on this new dataset utilizing the standard sack of words approach with the general execution of 44. 5%. Supposedly, UCF101 is right now the most testing dataset of activities because of its enormous number of classes, countless clasps, and furthermore unconstrained nature of such clasps. Figure  5  shows the 101 actions of the UCF101 data set in a single frame.
Results on UCF-101 Dataset The UCF-101 dataset is the most testing one, incompletely because of the enormous volume of video and somewhat because of the high level of perspective varieties with which the video is gathered. This isn't basically an observation dataset rather the recordings were gathered to assess normal movement acknowledgment errands. Given an inquiry video, the goal is to recover a video of a similar movement independent of the perspective varieties, and changes in hues or surfaces, and so on. The recordings in this dataset were taken with shifting foundations, which make it a fine possibility for assessing the reasonableness of our technique. We extricated CNN highlights utilizing AlexNet from these pictures of the recordings and utilized them to recover the top-positioned video.
The goal was to recover however many pertinent recordings as could be allowed. Figure  8  contains aftereffects of picked question recordings, where the furthest left video is the inquiry and the remaining are top-positioned video dependent on the separation between the question and the dataset video. Essentially, in inquiry 2, the best 6 pictures have been recovered accurately. In the third inquiry, applicable pictures were recovered at positions 2, 4, 5, 6, and 8, regardless of the way that there exists a tremendous divergence in their experiences. In the remainder of the inquiries, pertinent pictures were recovered at top positions which show the abilities of proposed highlights. Despite the fact that the outcomes on this dataset are not solid, we accept that if an all the more remarkable CNN model is utilized, these outcomes can be significantly improved.
In figure  8  the experimental result on dataset UCF101 shows on one video 1, 2, 3, 4, and 5. It made 6 numbers of clusters for all videos. From the FC6 simulation result, we obtained 44% accuracy and 56% loss. This layer of the AlexNet model gives very poor performance. In Figure  9  the experimental result on videos 1, 2, 3, 4, and 5. It made 6 numbers of clusters for all videos. From the FC7 simulation result, we obtained 60% accuracy and 40% loss. This layer of the AlexNet model gives poor performance. This 20% good than FC6. In Figure  10  the experimental result on videos 1, 2, 3, 4, and 5. It made 6 numbers of clusters for all videos. From the FC8 simulation result, we obtained 90% accuracy and 10% loss. This layer of the AlexNet model gives poor performance. The proposed method test on the UCF101 data set and give very good results on layer FC8.
Retrieval Performance of Videos The system performance is evaluated on the matching of input query and database query by subtracting the input query from the database query. The difference between the two videos found through the Euclidean distance formula. The keyframes are the head of the clusters. These keyframes applying the AlexNet model to extract features. For the performance evaluation, different types of videos are tested. For presentation, a different type of videos is acquired from UCF101 including shorts movies, military documentaries, music videos, and cartoon videos. The video length is 1-2 minutes. Different keyframes are selected from videos and save features in the feature vector. The redundant frames are skipped during the reading of videos. The same videos are searched on the selected keyframes.