dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying and Encoding Environmental Information As described in 1K, objects near the water surface preceding each swim bout are identified with image processing routines. Following image translation and rotation, a stack of the 6 images preceding bout initiation are cropped as in 1K. High spatial contrast objects are identified in Matlab by filtering each image in this stack with a Laplacian of Gaussian 2-D filter (size = 13 x 13 pixels, sigma = 1. 6). Contiguous 3-D object volumes in this image stack are smoothed with 3-D dilation and erosion. The average position of each object in the image frame preceding bout initiation was used to encode object location with the extrinsic feature v loc. The number of pixels assigned to each object in this final frame was used to encode object size with the extrinsic feature v size.
Supplementary Figures Figure  S1:  Encoding larval zebrafish posture in each image frame. A. In every image frame of our processed dataset, we estimate several postural features. Each image is first translated and rotated to align the fish head center (open black circle) and heading direction (solid black arrow) to a common reference. Left and right eye vergence angles are found by fitting an ellipse to each eye and comparing the direction of the minor axis of each ellipse to the fish heading (as shown). In this example, left eye vergence angle = 10°and right eye vergence angle = 30°. Eye vergence angles range from around -5°(highly diverged) to 45°(highly converged). Mean eye vergence angle is the average of the left and right eye vergence angles (20°in this example). Gaze direction is found by bisecting the angle formed between the minor axes of the eye ellipses, and quantifies the extent to which the fish is preferentially looking toward its left or right side.
E. Each image frame from 200 ms swim bout from D is aligned to maintain head position and heading direction. F. Fish head position (white circle), heading direction (purple arrow), eye vergence angles (angles between black arrows and horizontal), and tail shape (20 local tail tangent angles indicated with black carets) are extracted in every image frame (shown here for frame indicated in E). See S1 for posture estimation details. G. Histograms of bout and interbout durations from whole dataset. H. Histogram of heading direction change per bout (∆heading) for all bouts. I. Histogram of mean eye vergence per interbout for all interbouts. For each interbout, all eye vergence measurements are averaged. Larvae typically explore with eyes diverged and hunt with eyes converged. Local minimum at 24°(dashed line).
J. The field of view in the video dataset is large enough to see the fish and its local environment (circle diameter = 8. 12 mm, size indicated as circle within orange square in B). The focal plane is near the water surface. K. Locations, sizes, shapes, and relative velocities of all found surface objects preceding swim bout initiation are extracted. Relative motion of 3 objects are shown. In the next image frame, the fish initiates a swim bout (shown in 2B) to pursue the prey object in box 2. Found objects are mostly edible (paramecia, rotifers) but include also algae, dust (like in box 1), and some artefacts. Box width = 820 µm.
Acknowledgements Research was funded by  NIH  grant  U19NS104653  and  Simons Foundation  grant  SCGB-325207. S. L. was supported by a  Simons Collaboration on the Global Brain postdoctoral fellowship  ( SCGB-418011 ) and the Siebel Scholarship. We thank  Ed Soucy ,  Joel Greenwood , and  Adam Bercu  of the  Harvard Center for Brain Science Neuroengineering Core  for technical support. We thank  Andrew Bolton  for helpful discussions on pose estimation and prey quantification,  George Dimitriadis  and  Adam Kampff  for assistance with GPU implementation of tSNE, and  Matthew Johnson  for helpful modeling discussions.
Fitting the Model We fit the model with maximum a posteriori (MAP) estimation. We computed the log joint probability density, where the first term is the log likelihood from (4) and the second is the prior from (8). We used Autograd (https://github. com/HIPS/autograd/) to compute its gradients with respect to the model weights. We optimized the log joint probability with Newton's method, using the conjugate gradient method to solve the linear system in the Hessian matrix ∇ 2 log p( , | ), as implemented in SciPy. Once the MAP estimate of the weights was obtained, we computed a Laplace approximation to the posterior distribution, where the inverse covariance matrix is the negative Hessian at the mode, (Note that we have committed a slight abuse of notation by treating as a vector rather than a set.
We performed several analysis under (i) the control flow perspective, which focuses on the different activities carried out by patients during the careflows, (ii) the organizational perspective, which describes the relationship between the hospital resources through social networks, and (iii) the performance perspective, which looks at the timing perspective of the different activities involved in the patient careflows. We analyzed three different datasets, corresponding to three different clinical processes (i. e. , 'Outpatient Clinic', 'Emergency Room' and 'Hospitalizations'), provided by the hospital in accordance with the privacy regulations. The clinical datasets contain raw data about patients treated in the year 2017. Provided data are the ones required by the Italian National Health Service, which any hospital located in Italy must exchange with the Public Regional Authorities.
Thus, the proposed analysis can be potentially repeated nationwide. In addition, it is worth to notice we conducted the analysis leveraging just on the available datasets, without having at hand any pre-defined schema or model describing the patients' careflows. Results obtained on the proposed case study prove the usefulness of applying process analysis and mining for the governance of healthcare facilities. In particular, managerial staff were able to quantitatively evaluate the results of recent investments, consisting in the construction of a new block of the hospital, which encourage new investments in the next future. Additionally, clues have been obtained, which will be helpful to address the problem of treatment abandonments in the emergency room. The rest of the paper is structured as follows.
However, available clinical datasets are not usually stored using this format, thus requiring a preprocessing phase that we will detail in the following section. Process mining addresses the needs of most system/process owners, which have limited information, or only intuitions, of what are the performance and the bottlenecks of the managed processes. This happens because there is often a significant gap between what is supposed to happen, and what actually happens. Only a concise assessment of reality, which process mining strives to deliver, can help at verifying processes, and ultimately be used in system/process redesign efforts and governance. The idea behind process mining is to discover, monitor and improve real processes by extracting knowledge from event logs. FIGURE  1  shows how process mining can be applied to the hospital context.
A hospital is a physical facility where different resources perform treatments on patients supported by an HIS (Hospital Information System) distributed on different kinds of devices ranging from personal computers, tablets and medical equipment. The work of resources inside the hospital is driven by some kind of implicitly or explicitly defined processes, whose instances are recorded in the databases of the HIS and can be turned into event logs for process mining. Once event logs are available, they can be used as input for the different process mining tasks, which are usually classified in the categories of discovery, conformance checking and enhancement. Discovery aims at deriving information about the original process model, the organizational context, and execution properties from event logs, without any a-priori model in input.
In addition, Bose and van der Aalst  [23]  evaluated the applicability of process mining techniques on a real-life event log, taken from a Dutch Academic Hospital, that contains events related to treatment and diagnosis steps for patients diagnosed with cancer. Some authors conducted their case studies relying on tools different than ProM. For instance, the work  [24]  presents a case study carried on with Disco. 1  The authors started from a database of the accesses to the services in a local health agency, and produced the activity flow diagram describing these services. In  [25] , the authors presented a case study formalized with the Declare language  [26] , with the aim to to show that declarative models are more suitable than imperative ones for describing care processes, which are usually highly unpredictable and unstable.
B. DATA PROCESSING The hospital provided us with three clinical datasets: • Outpatient clinic: each row of the dataset stores the information about a single clinic service associated to a patient. A patient with a medical prescription related to a specific healthcare service can go in the corresponding outpatient clinic either with or without the reservation. The total number of records stored within the dataset amounts to 299685. • Emergency room: each row of the dataset represents a single emergency room activity associated to a patient. Once arrived in emergency room, a patient undergoes a first diagnosis and then, depending on the outcome, s/he could be: (i) sent home, (ii) hospitalized, or (iii) transferred to another institute. The total number of records stored within the dataset amounts to 22043.
• Hospitalizations: each row of the dataset represents a single hospitalization associated to a patient. Patients may undergo surgery or may receive other types of clinical procedures. The access to hospitalization wards can take place in different ways. The total number of records stored within the dataset is of 10843 records. The original datasets contained a sequence of rows consisting of fields whose values were encoded. Additionally, data contained several dirty records consisting of incorrect, missing, imprecise and/or irrelevant data and duplicates. Due to these issues, we converted the datasets from TXT format into and intermediate CSV format in order to filter out the aforementioned issues. Table  1  shows excerpts of the three intermediate CSV datasets. For each dataset only a small subset of fields is show for the sake of readability.
C. DATA EXTRACTION After cleaning the datasets, the extraction stage aims at extracting event data from the raw data. To achieve such objective, the PM 2 methodology suggests to answer the following questions: (i) which is the granularity of the extracted events? (ii) which time period should be considered? (iii) which data attributes should be extracted? (iv) how should data fields be correlated? This phase, also called scoping, is critical and high risk, being it completely manual, tailored on the specific data source and affecting all the downstream phases. In order to build the event logs, there is the need to understand the relevance, the granularity and the variability of each data field of the clinical datasets. Pie charts can be useful to this end, helping the enumeration of all the possible values that a data field can assume.
In this way, it is possible to understand to which fields it is possible to associate the concept of event. Once scoping was completed, three event logs were generated, one for each dataset, by making a correspondence between certain fields and events. Each source dataset row can indeed correspond to multiple events, as clinical activities are denoted by date fields and specific activity codes. Looking at Table  1 , examples of such fields can be found. FIGURE  3  shows an example of how such correspondence between fields and events can be obtained. Starting from the data field 'healthcare provision date', it is possible to create the healthcare provision activity associated with a case id (the encrypted fiscal code of the patient concatenated with a sequential number), with a timestamp (the value of the raw data field 'healthcare provision date'), and with the resource that is executing the event.
D. MINING & ANALYSIS In this phase, we applied process mining and process analytics techniques on the three XES event logs obtained through data processing and extraction from the original clinical datasets. In order to apply process mining, different frameworks and toolkits do exist. In this article, we employed ProM  [6] , an extensible open-source framework where various process mining algorithms, in the form of plugins, have been implemented. It is platform independent as it is realized in Java. The framework provides easy to use user interface functionality, a variety of model type implementations (Petri nets, BPMN notations, YAWL, etc. ), the loading and filtering of event logs, several mining algorithms to be applied and the corresponding visualization of results. Notably, ProM allows the chaining of different plugins to obtain complex result starting from one or more complaint objects (i.
• When the interaction between resources is of interest, the Social network miner  [45] ,  [46]  has been employed. 2 http://www. promtools. org/doku. php?id=prom67 • If events must be represented directly, the ''Dotted chart analysis'' plugin  [44]  has been used. Initially, we focused on gaining general insights about the careflows. In particular, different views (versions of the dataset) have been created, by varying the definition of event. This is due to a limitation of the XES standard, and in particular in its organizational extension  3  with respect to the notion of resource. This extension and, as a consequence, all the process mining algorithms currently available in ProM, allow to associate to each event a single resource. Conversely, in hospitals, there are often different resources involved in each activity and, as a consequence, associated to each event: human resources (of different categories), instrumental resources and infrastructural resources.
1) Q1 -OUTPATIENT CLINIC -RADIOLOGY DEPARTMENT The XES event log derived from the outpatient clinic dataset contains activities of three categories, namely (i) the emission of the medical prescriptions, (ii) the booking of healthcare services, and (iii) the execution of these services. Each of the service is performed by a different sub-department. Since the event log contains all the healthcare services of the hospital, we filtered it by considering only those provided by the radiology department thanks to the ''Simple log filter'' plugin of ProM. Among the different discovery algorithms in ProM, here the Inductive Visual miner was employed because it produces structured models and it is able to recognize repetitions of activities of single cases. FIGURE  4a  shows the resulting process represented as a BPMN diagram, where we only focus on service execution activities.
Here, it is possible to see the number of patients involved in each radiology sub-department. In the observed period, a total of 7912 people used the services of the radiology laboratory. In particular, 1052 patients passed through the Orthopaedic Cast room, 1362 through BMD -Bone Mineral Density, 1674 through CAT -Computerized Axial Tomography, 2700 through Eco 2, and 2355 through Ultrasound Radiology, with possibly the very same service repeated on the same patient multiple times (e. g. , 1049 patients repeated the ECO 2 analysis). Through social networks analysis (see FIGURE  4b ) it emerged that patients can potentially use all resources within the radiology outpatient clinic. The social network shows that the relationships between the resources are many-to-many, which means that the observed dataset describes an interaction between all the possible pairs of resources provided in the dataset.
2) Q2 -EMERGENCY ROOM -PATIENTS' ABANDONMENTS With respect to emergency room activities, we focused on the phenomenon of patients leaving the emergency room before or while the medical examination. The XES Event log derived from the 'Emergency room' clinical dataset contains the following types of activities: (i) the arrival of the patient in the emergency room, (ii) the medical examination and (iii) all the possible outcomes of a medical examination. FIGURE  5a  depicts the careflow mined with the ''Inductive Visual miner'' plugin (this time no BPMN representation has been extracted). In total, 857 patients leave the emergency room before the medical examination (denoted with the ''disappeared'' activity) and 477 in the middle of it (denoted with the 'go away' activity). For a further investigation, as requested by the hospital management, we leveraged on process analytic techniques in order to study the hourly distribution of the 857 'disappeared' patients.
3) Q3 -HOSPITALIZATIONS -OPERATIVE BLOCKS Regarding hospitalizations activities, we focused on obtaining insight on the differences between the new operative block (operating rooms of block B2) compared with the old one (operating rooms of block B1). In particular, we paid attention to the temporal perspective of the surgical interventions. The XES event log derived from the 'Hospitalization' dataset contains the following types activities: (i) the arrival in emergency room, (ii) the medical examination, (iii) the hospitalization reservation, (iv) the hospitalization, (v) the surgical intervention, (vi) the hypothetical transfer from a hospitalization ward to another one, and (vii) the discharge. As depicted in FIGURE  7a , the operating rooms of the new block (B2) have been activated in the second half of the year to replace the old operative block (B1).
Additionally, the hospital management is interested in further analysis regarding the economic aspect of the hospital. Indeed, they will provide us with new data to be integrated with the clinical datasets containing the economic values associated to each surgical intervention. The goal of the hospital management is to define the concept of revenue associated to each patient classified by DRG -Diagnosis Related Group code. The DRG codes represent the instrument useful to classify the final product of the hospital (i. e. , the overall work) for the purpose of remuneration of the hospital activity.
V. THREATS TO VALIDITY AND CONCLUSION In this article, we conducted a process mining project based on a real case study of an Italian hospital in Rome. In particular, starting from the clinical datasets (containing anonymous records) we were able to discover non-trivial care processes, social networks and transition systems relying on the ProM framework. We gave insights about the process, describing the patient careflows within the hospital, under different points of view by changing the concept of event from time to time. We have also looked both at the organizational perspective of the hospital, describing how the internal resources can communicate among them, and on the performance perspective, analyzing for instance the duration of the surgical interventions. The presented experimentation is conducted starting from the clinical datasets of the Italian National Health Service that the hospitals interchange with the Public Regional Authorities.
As a consequence, the same experimentation can be replicated nationwide establishing a benchmarking between the various healthcare organizations or between different divisions of the same organization. The management of a hospital can easily obtain, thanks to process mining techniques, value-added information that were previously difficult to identify or even undetectable. The entire process mining project is anyway very sensitive to data preprocessing. Available datasets, as discussed in the paper, are usually not represented in the form of events, i. e. , as activities performed by specific resources. Conversely, they are represented in a report form where activities must be argued. In this sense, the governance of the process mining project is of utmost importance, requiring a continuous interaction between data analysts and stakeholders.
A powerful tool such as process mining could, in fact, not lead to tangible results for the organization if it is not correctly applied in the organizational areas (and data) in which the root causes of the identified issues persist. Additionally, clinical datasets contain noise and duplicates that must be removed or at least identified prior to apply process mining. This task may require techniques that are outside the scope of process mining, being instead taken from the wider world of data mining and very similar to what ETL -Extract Transform Load -is for datawarehouses. Thus, to properly apply process mining techniques, some extra effort is required at the outset for pre-processing clinical datasets, but the overhead is compensated by the possibility of automatically discovering and diagnosing healthcare processes.
Another limitation concerns that the employed datasets keep track of events happened in the span of two months between the end of 2015 and early 2016. For this reason, to confirm the validity and robustness of our findings, we aim to employ the same process mining techniques shown in this study over novel (and more recent) datasets recorded in a longer time frame. Given the above weaknesses, our discussions should be considered as indicative observations rather than conclusive statements. In conclusion, the lesson learned through the presented case study is that the availability of event data combined with process mining techniques can lead to significant results in terms of process analysis, useful for the hospital management to take final considerations, diagnose problems and set improvement initiatives based on real facts, represented in form of clinical data.
It runs specific tests on diverse installed browsers and return results, alerting you to failures in browsers as they crop up. However, automated web-based testing tools like Selenium still have major drawbacks. Indeed, tests have to be repetitive and require resources and a lot of maintenance. A very small change or modification in the content of a web page may cause a test fail. Furthermore, many tests scripts are manually handled which increases the costs and time for testing  (Shariff et al. , 2019; Ameur-Boulifa et al. , 2019). Researchers and industrials are studying and defining optimized approaches to deal with this challenge. Two current popular research areas for automation web-based testing are Codeless Functional Test Automation and Machine Learning/Artificial Intelligence (ML/AI) in test automation.
In here, algorithms are defined and implemented to figure out how to automate the software/web service product under test, the machine can detect or predict the change and adapt those changes to the suitable test cases. Thanks to codeless testing, testers can drastically reduce the cost of test automation creation, test execution, and maintenance. In our work, we examine on Selenium and the benefits of using machine learning in automated web application testing. The motivation is to utilize the selenium framework in the world of automated testing and utilize a machine learning approach for codeless testing, enabling to test multiple functional web pages without using code. The main contributions of our paper are: • Defining a framework named "codeless testing" by combining Selenium and machine learning technique, • Implementing and applying our framework to run generic test cases in order to test the functionality search that is present in multiple web pages, • Through our experiment results, we show that the success of generic test cases may help to contribute in decreasing implementation and maintenance costs of automated tests of functional browser based applications.