dataesr/scientific-paragraphs-categorization huggingface dataset:
If the selected line intersects the Harris corner, then the values of both slops (i. e. , m h and m) will be equal. Similarly, the value of d provided the information that how far is the Harris corner from the initial point of the selected line. For the proposed technique, the size of six selected patches for ROI, their locations, and the values of other parameters such as MðiÞ, a h , and h are kept the same for all types of road scenarios and light conditions. The same values are used for the self-recorded and online datasets. Each dataset has different frame sizes and a different placement of the camera; therefore, the fixed area that has been excluded from the top of every grayscale frame varies for each dataset. Last, the binary road image is used to make a decision whether the selected lines represent a vehicle or not.
Experimental Results The given technique was implemented on the Intel ® core ™ i7-4770 CPU (3. 4 GHz processor dual core, installed memory 16GB). Our technique was investigated in C++ using OpenCV. Our method was evaluated on the selfrecorded dataset as well as on the online datasets  [43] [44] [45]  for comparison purposes. The results show that our technique works perfectly well for the motorcycle (using the selfrecorded dataset) as well as for the online car datasets. It can detect both incoming and outgoing vehicles perfectly from the rear-end on single and multiple lanes. The technique is also capable of detecting parked vehicles on the roadside. Some of the results from the self-recorded dataset are shown in Fig. 11. In Fig. 11 , the detection of rear-end vehicles is shown for the self-recorded dataset.
Also for each technique, an average frame rate processing time has been computed, and it is equal to the average number of frames processed in 1 s. From Table  3 , one can see that for the self-recorded dataset, the accuracy is 95. 1%. When comparing with the existing state-of-the-art works, our technique shows improved performances on all the quantitative criteria. The reason for a better FDR is due to the patch selection method, which enables to predict an enhanced binary road region. The second main reason is the length of the selected Hough lines, which helps to remove the shadow regions. Our technique achieves a high accuracy for source-2 dataset with respect to the work of Choi. 45 For LISA-dense and LISA-urban datasets, our method achieves higher TPR as compared with the recent research work of Sivaraman  43  and Satzoda and Trivedi.
51 For LISA-Sunny dataset, our technique provides a TPR that is a bit less as compared with the techniques of Sivaraman  45  and Satzoda and Trivedi. 51 owever, the FDR of our method on LISA-sunny dataset is less compared with the existing work. For iROADS-daylight dataset, our technique's TPR and FDR are lower as compared with the work of Satzoda and Trivedi,  51  but a higher frame rate is achieved. Satzoda's 51 technique performs better under sun light condition, whereas our technique performs good for all light conditions and traffic scenarios. The proposed work achieves higher frame rate for the self-recorded dataset and for the online datasets as compared to the all existing methods. 43, 45, 51 The rapid vehicle detection gives more time to the motorcyclist to take a correct decision, therefore making it more suitable for motorcycle applications.
Conclusion From this work, it can be concluded that the proposed technique is effective for rear-end vehicle detections for motorcycle applications. The method presented in this paper achieves higher accuracy and better results in different road scenarios compared with other methods recently published. The patch selection method for the binary road detection contributes a lot to reduce the false detections and produce reliable results, even when shadows or different lane markers are present on the road. The size selection of the lines, computed from Hough transform, also helps to avoid the shadow regions and improve the accuracy. It shows a very good performance for the motorcycle using the rear-end dataset as well as utilizing the online vehicle frontal datasets. The proposed method provides a reliable accuracy, making it more trustworthy for vehicle applications.
Acknowledgments We express gratitude to and acknowledge the  Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia. This research is partially supported by the  Ministry of Education Malaysia under Higher Institution Centre of Excellence (HICoE) Scheme, Centre for Graduate Studies (CGS) , and  University Research Internal Fund  ( URIF-015AA-B78 ) of  Universiti Teknologi PETRONAS, Malaysia. We also would like to express our gratitude to the  ERASMUS Mundus program  VIBOT for sponsoring some of this work.
) 4. 1. 2 Fig. 8 4128 Fig. 8 Lane marker free (LMF) image: (a) self-recorded dataset with multiple vehicles footprints, (b) selfrecorded dataset with truck at lane markers, (c) LISA-dense (frame 735), (d) LISA-urban (frame 13), (e) LISA-sunny (frame 104), (f) source-2 (frame 140), (g) iROADS-daylight, and (h) iROADS-tunnel.
EXPERIMENTAL RESULTS In this section, the performances of the proposed algorithm appSVD+LDA are given. The experiments are based on real data sets including face recognition and text classification which can be download at http:// www. cad. zju. edu. cn/home/dengcai/Data/data. html. All the experiments have been performed on P4 2. 7GHz Windows7 machine with 16GB memory. We have used Matlab routine for programming.
Data Sets Two images data sets ORL, COIL20 and two texts data sets TDT2, Reuters21578 have been used in our experiments. The image data have been normalized to have L2-norm equal to 1. For text data, each document have been represented as a term-frequency vector and have been normalized to have L2-norm equal to 1. The statistics of these data sets are listed in Table 1. COIL20. This data set contains 1440 sample images of 20 different subjects. The size of each image is (32 × 32) pixels. ORL. This data set contains 10 different poses of 40 distinct subjects with 4096-dimension (64 × 64 pixels). The images were taken at different times, ranged from full right profile to full left profile.
As the dimension in this case is relatively large, the computation time for DLDA is very large (see Table  5 ). Reuters21578 and TDT2 are very large data sets. As DLDA needs memory to store the centered and scatter matrices in the original features space, it is infeasible to apply DLDA in these cases. Tables 6 to 9 display only the performance results for NovRP, LDA/QR and appSVD+LDA. The NovRP method gives the most efficient time (see Tables  7  and 9 ) whereas its accuracy is by far the lowest. It can be seen that the chosen value of p is widely sufficient for appSVD+LDA to recover nearly 86% of accuracy for Reuters21578 and 95% for TDT2 and the computational time is quite small (see Tables  7  and 9 ). In the whole results appSVD+LDA significantly outperforms LDA in running time and its accuracy performance let believe in its effectiveness and efficiency compared to other methods.
CONCLUSION This work provides a novel approach to tackle the problem encountered when performing LDA with large scale data sets. It consists of looking for an approximation of the original space in a lower rank. It combines the fast approximate singular value decomposition and LDA. We show by experiments on real world data sets the effectiveness and the efficiency of the proposed method appSVD+LDA. As can be seen, appSVD+LDA outperforms direct LDA in terms of computational time and achieves significant performance in comparison to other state-of-the-art methods. appSVD+LDA allows to classify large scale data by just holding a small features size (k). For example, on Reuters21578 data set where the original feature space is d = 18933, it achieves more than 86% accuracy in nearly two seconds whereas it is infeasible to perform direct LDA in this case.
Introduction While data analysis has scaled dramatically in the last decade, this scalability has only impacted "confirmatory" data analysis, or model-based analysis, i. e. analyses where the structure of the data is known in advance, as well as the best algorithms for this analysis. For exploratory data analysis  [Tuk77]  and its modern incarnation visual analytics [Tho05, KAF * 08], scalability remains limited due to the latency of traditional analysis systems. These systems typically yield long response times when analyzing large datasets or when using complex algorithms. Such latency is problematic in visual exploration since humans cannot maintain their attention when system response time exceeds approx. 10 seconds  [Mil68, Shn84]  and even small delays can affect their insights  [LH14].
In addition, PVA ensures these advantages irrespective of the scale of the data and computation. In this article, we focus on the human side of the PVA paradigm, especially in understanding (1) how visualizations can be adapted to better support PVA, and (2) how the user interface of PVA systems should be designed to provide the feedback and control needed to make early decisions reliably, and its effects on sensemaking. We answer these research questions through an analysis of the design of a PVA tool called INSIGHTSFEED that we developed for analyzing large Twitter datasets. InsightsFeed adapts the traditional visual analytics pipeline  [KKEM10]  to incorporate intermediate results and feedback about progress from long computations, while providing easy controls to change the parameters of the progressive computations as well as supporting interactive filtering of visualized data.
Motivating Example To understand the potential advantages of PVA, let us consider a data journalist who wants to understand the conversations on Twitter about the presidential candidate debate in the U. S. , and write an article about it. For this analysis, tweets are collected during the debate using the Twitter API. A traditional analysis involves classifying sentiment, identifying popular users, and finding important keywords that stand out to make sense of the conversations  [TSSW10, YGRP12]. This analysis process takes a time proportional to the number of tweets; and depending on the complexity of the computations, it can take hours. In contrast, our InsightsFeed system uses a progressive approach where the dataset is loaded by chunks of hundreds of tweets and the intermediate results of the analyses are updated every few seconds on the screen.
Progressive Visual Analytics Hellerstein et al. [HHW97]  introduced the concept of progressive data analysis; he showed that, when computing aggregate queries in a database (e. g. , mean of a column), the database could return estimates that improve over time instead of waiting until the computation finishes. The concept was meant to avoid latency and monitor the results through simple visualizations with confidence intervals, allowing users make confident judgments for early decisions. The principle of reducing the latency by computing improving estimates has then become popular under multiple names (iterative, online, streaming, any-time)  [FP16] ; the term "progressive" avoids ambiguity. Furthermore, early progressive systems have provided interactions for steering the computations  [MvWvL99].
They showed that users performed equally well with instantaneous and progressive visualizations in key metrics such as insight discovery rates and dataset coverage, while blocking visualizations had detrimental effects. Designing PVA interfaces implies following important requirements to overcome the algorithmic issues and optimizing the human aspects. These requirements have been elicited by  Stolper   While these requirements are developed to be generally applicable to PVA scenarios, the extent of their application varies with the context. For instance, when visualizing simple statistical measures  [FPDS12] , the need for steering results, providing controls, and provenance is minimal compared to a PVA interface running multiple flexible algorithms (e. g. , similarity search  [SASS15] ).
PVA systems therefore balance their UI features to support more than one of these requirements at the same time. For example, quality as the relative progress measure in Incremental Visualization  [SASS15]  also implicitly captures the uncertainty the result. Result and execution control: Hetzler et al. [HCPT05]  discuss about "providing full set of interactions that are available with regular or non-dynamic datasets" (R12). In PVA, filtering options to focus on subspaces are supported by ProgressiveInsights  [SPG14]  and VizDom [ZGC * 16]. R13 "on-demand refresh" is a feature of ProgressiveInsights but other systems refresh the views along with the computation. We return to this specific point in Section 7. 3. Incremental Visualization  [SASS15]  provides a set of control buttons that can change the parameters to steer the algorithms (R14) on the fly or stop the progression entirely (R15), while Sam-pleAction  [FPDS12]  allows pausing and stopping the processes (R15).
The InsightsFeed System We created the InsightsFeed system to explore large textual datasets from social media such as Twitter, Reddit, and Facebook using the PVA paradigm. While the system is generally applicable to other social media, here we focus on Twitter. The datasets typically consist of tweets (short texts in general), replies, retweets, author information, and some metadata (e. g. timestamp, location, tags). As explained in our motivating example, the InsightsFeed system consists of four connected views (Figure  1 ). These views are driven by an integration of progressive computations and progressive visualizations, which together make up a PVA model. Here we explain the progressive visualizations and their computational methods, and then describe the user interface elements, visual representations, and controls added to support the PVA paradigm, following the requirements listed in the Background section.
The computational methods used in InsightsFeed are adapted to have a progressive nature through simple mechanisms to work incrementally with chunks of data, build on data structures from previous steps, and maintain stability. However, some of the algorithms used-t-SNE and k-means-need further modifications to their process logic to be fully progressive. While progressive algorithms would improve the computation throughput, the UI aspects explored in this paper would remain the same since our visualizations apply aggregation and sampling scalable to large datasets. The system starts by reading a dataset, tweet by tweet, from a local file. The tweets are stored in random order to improve the convergence of progressive algorithms. Each tweet is pushed into a chunk; when it is of a particular size, it is passed to the computational methods that send the results to the visualizations (Figure  3 ).
Keyword Extraction: The tweets are treated as documents; while it may appear odd to treat 140-character sentences as documents, twitter datasets following real-life events are often rich with keywords related to the event. Therefore, inverse document frequencies can be computed when enough chunks have been loaded. The term frequencies (tf) and inverse document frequencies (idf) of the words are computed to generate a tf-idf score for each word in the tweets  [SJ72]. Stopwords are removed, and only nouns, adjectives, verbs, and adverbs in their base form are considered. The words are then sorted by their tf-idf scores and the top words are picked as keywords. The chunk containing the tweets, authors, timestamps, keywords, and sentiment information is passed to the list view. Progressively visualizing tweets: To minimize disrupting the user's workflow (R3, R4, R5), the tweet list is updated with each chunk by a two-step process: (1) each tweet is inserted in timestamp order and highlighted with a gray background, and then (2) tweets from previous chunks are deleted starting with the oldest chunk until all the new tweets fit into the view.
UI Elements for Feedback and Control Based on the guidelines from Mühlbacher et al. [MPG * 14], all the progressive visualizations are coupled with additional UI elements and small visualizations to provide feedback and control (Figure  6 ) over the computational methods. Progress Bars: Each progressive visualization has a progress bar that captures the number of tweets processed to get to the current state of the progression (R10). However, recall that the entire system is driven by a dataset reader that reads tweets line-by-line. Therefore, the total number of tweets in the dataset used for populating this progress bar is also a progressive measure that is estimated based on the total lines read, total bytes read, and size of the file in bytes. Each visualization in the system can have a different progress as the computations take different amounts of time and the user can influence the processing speed.
A blinking rectangle is placed beside the progress bar to highlight the current chunk being processed by the computations. This satisfies requirements R9-R10, as the user is informed of the progress and the aliveness. From an UI design point-of-view, other absolute progress measures can also be used for this feedback, based on (1) time spent and total time left, (2) bytes read and total size of the dataset, and even (3) the error metric of the computations and target error.
Mapping Requirements to User Interface InsightsFeed satisfies the requirements presented in Section 3. 2. It provides meaningful partial results that are structure-preserving for all the progressive visualizations (R1, R2). This is done by shuffling the dataset ahead of time. The updates are made through a transition process for each visualization depending on its content (R3, R4, R5). Our visualizations (except Tweet List) use aggregation (e. g. , heatmap) (R6) and also extract important information (e. g. ,.
Hover Select Figure  7 : Interactions with the tweet map: hover on a cell to get the keywords, and select a region to update the interface. top users) to provide representations scalable to large datasets. Progress bars are attached to each view along with an aliveness indicator and a quality curve to convey execution feedback (R9, R10, R11) and uncertainty (R7), reinforced by history navigation using the playhead. The user interactions (Figure  7 ) allow exploration of subspaces (R12) and further support visual analysis. The control options are designed to update results when wanted (R13), steer computations (R14), and cancel/update the execution (R15, R16).
Participants We recruited 10 participants (2 female, 8 male), who had previous experience with data visualizations. Participants were between 18 and 45 years of age and recruited from our neighboring HCI/visualization research labs. All participants self-reported as proficient computer users. Data analysis experience varied across participants: all were experienced with creating charts for reporting, 3 participants worked in positions that require analyzing specific datasets, and 5 participants developed visualization tools.
Dataset and Task Given the global interest in the United States presidential election, we decided to pick datasets from the Twitter accounts of Hillary Clinton and Donald Trump. Since we were studying two different interfaces-progressive and instantaneous-we collected two sets of random tweets (about 2,900 each) from their accounts from April to December 2015. Having two datasets enabled a within-subject design. We created a list of five tasks (questions) for each dataset that involved each of the four views in the interface. All tasks were given at the start and could be answered in any order. Q1 What is the most frequent sentiment in the dataset? Q2 Who are the four users making the most tweets? Q3 What are three most frequent words on the tweet map? Q4 What is a representative tweet that captures the candidate's stance on immigration (Trump) or rights (Clinton)? Q5 What are the words associated with "health" (Clinton) or "border" (Trump)? Pick three words and also identify the candidate's sentiment with them.
Procedure During each experiment session, the participants started with their assigned interface and dataset. They then went through the training procedure for the assigned interface. For PVA, we explained the participants the four levels of features on the interface: (1) individual visualizations, hover and select interactions, and the animations during each iteration of the progression; (2) progress bars and quality visualizations on each visualization and what they represent in terms of the computational method behind the visualization; (3) playhead controls on the progress bars to go back in history and see previous states of the visualizations; and (4) controls over the computational methods and progression including processed chunk sizes, speed of progression, and number of clusters on the tweet map.
The progression parameters are initialized based on the visualization content-Tweet Map updates less frequently than sentiment visualization to give the user more time to interpret it. Training on the PVA interface lasted about 15 minutes. Participants were then quizzed about features to make sure they understood them and the explanations were repeated. On the IVA interface, a similar procedure was followed to explain the visualizations. After finishing one interface and dataset, the procedure was repeated for the other. After training, the participants began answering questions for the assigned condition. For the PVA interface, the progression of the visualizations was started immediately. Participants were encouraged to answer the questions whenever they had an answer; they were asked how confident they were with their answer, if they would wait for more iterations to give a more confident answer, and what would improve their confidence.
Explaining the Results All participants made multiple early observations before reaching a final answer. However, the behavior diverged on the type of early observations. The type of observations and confidence mainly relied on the feedback elements in InsightsFeed-the visualized quality and the progression stability. The early observations may also depend on the individual preference-some people are more comfortable giving partial answers than uncertain ones. While early observations were eventually developed into final answers, complete but uncertain answers sometimes revealed additional information about the dataset. For example, early answers about frequent words sometimes indicated words that were frequent in only part of our dataset, e. g. "golf" and "business" for the Trump dataset.
Limitations • The datasets in our study were small. However, we believe that they were complex enough to create realistic tasks and observe the usage of our PVA interface. Furthermore, the visualizations and UI elements in InsightsFeed use aggregation and sampling to scale to larger datasets. Having said that, there may be differences in the time spent on monitoring progression and interactive exploration in case of larger datasets. However, our study results about the sensemaking process in PVA, including the evolution of the early observations, estimation of confidence, and the usage of UI elements, would still remain meaningful in these settings. • Our experiment focused on a particular set of tasks rather than on open-ended exploration. While we believe that these tasks were representative of a typical analytical process, future studies should focus on high-level exploration.
Particle size distributions were sampled thanks to a Scanning Mobility Particle Sizer (SMPS composed of a Grimm 5. 403 Condensation Particle Counter linked to a short Differential Mobility Analyser, Vienna type, GRIMM Aerosol Technik GmbH, Ainring, Germany), which allowed measuring the particle size distribution in terms of mobility diameters in the range [5-350 nm], with a sampling flow rate of 0. 3 l. min -1. The morphology of aggregates was documented through Transmission Electron Microscopy (TEM) analysis so as to provide additional data concerning the particles monomer size and their effective collision diameter and shape factor.