Recently, Temporal Information Retrieval (TIR) has grabbed the major attention of the information retrieval community. TIR exploits the temporal dynamics in the information retrieval process and harnesses both textual relevance and temporal relevance to fulfill the temporal information requirements of a user Ur Rehman Khan et al., 2018. The focus time of document is an important temporal aspect which is defined as the time to which the content of the document refers Jatowt et al., 2015; Jatowt et al., 2013; Morbidoni et al., 2018, Khan et al., 2018.

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It contains the four biomarkers which we have selected for the instrument, in the first column we have the recordings for heart, in second we have recordings for temperature, third is for muscle activity and last column is for oxygen levels.

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This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:

Instructions: 

This dataset can be used for building a predictive machine learning model for early-stage heart disease detection

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This dataset was collected from force, current, angle (magnetic rotary encoder), and inertial sensors of the NAO humanoid robot while walking on Vinyl, Gravel, Wood, Concrete, Artificial grass, and Asphalt without a slope and while walking on Vinyl, Gravel, and Wood with a slope of 2 degrees. In total, counting all different axes and components of each sensor, we monitored 27 parameters on-board of the robot.

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GesHome dataset consists of 18 hand gestures from 20 non-professional subjects with various ages and occupation. The participant performed 50 times for each gesture in 5 days. Thus, GesHome consists of 18000 gesture samples in total. Using embedded accelerometer and gyroscope, we take 3-axial linear acceleration and 3-axial angular velocity with frequency equals to 25Hz. The experiments have been video-recorded to label the data manually using ELan tool.

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This is an alarm management dataset based on the “Tennessee-Eastman-Process” (TEP). The presented dataset aims to provide a suitable benchmark for the development and validation of alarm management methods in complex industrial processes using both quantitative data and qualitative information from different sources. Unlike real industrial processes, the simulation of the TEP allows to design and generate abnormal situations, which can be repeated and varied without risking the loss of equipment or harming the environment.

Instructions: 

This dataset includes a supplementary technical report. The report starts with a brief overview of the facility, implemented control loops and used simulation model. Two types of data, process and alarm data, were collected from the facility, their underlying data structure is included. The process of developing and implementing suitable alarm thresholds and alarm management techniques is described in detail. Furthermore, all initiated abnormal situations and the respective test design is presented thoroughly. The report concludes with a detailed description of the dataset structure and layout.

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This data-set consists of 3-phase differential currents of internal faults and 4 other transients cases for Phase Angle Regulators (PAR). The transients other than faults include magnetizing inrush, sympathetic inrush, external faults with CT saturation, and overexcitation conditions. PSCAD/EMTDC software is used for simulation of the internal faults and the transients.

Instructions: 

The files - ’fault_location_pha’, ’fault_location_phb’, and ’fault_location_phc’ have phase a,b,c differential currents for 46872 cases. Each row has 167 samples (one cycle). In fault_location_target’- the first 33480 are faults in series unit, next 13392 are faults in exciting unit. The files - ’transients_pha’, ’transients_phb’, and ’transients_phc’ have phase a,b,c differential currents for 13680 cases. Each row has 167 samples (one cycle). In fault_transient_target’- the first 720 - overexcitation, next 2520 - magnetizing inrush, next 2520 -sympathetic inrush, and next 7920 are external faults.

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"The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself.

Instructions: 

The present data release contains the data of 2 subjects of the 3D-FLARE DB.

 

These data is released as a sample of the complete database as these 2 subjects gave their specific consent for the distribution of their 3D fingerprint samples.

 

The acquisition system and the database are described in the article:

 

[ART1] J. Galbally, L. Beslay and G. Böstrom, "FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing", IEEE ACCESS, vol. 8, pp. 145513-145534, 2020. 

DOI: 10.1109/ACCESS.2020.3014796.

 

We refer the reader to this article for any further details on the data.

 

This sample release contains the next folders:

 

- 1_rawData: it contains the 3D fingerprint samples as they were captured by the sensor describe in [ART1], with no processing. This folder includes the same 3D fingerprints in two different formats:

* MATformat: 3D fingerprints in MATLAB format

* PLYformat: 3D fingerprints in PLY format

 

- 2_processedData: it contains the 3D fingerprint samples after the two initial processing steps carried out before using the samples for recognition purposes. These files are in MATLAB format. This folder includes:

* 2a_Segmented: 3D fingerprints after being segemented according to the process described in Sect. V of [ART1]

* 2b_Detached: 3D fingerprints after being detached according to the process described in Sect. VI of [ART1]

 

The naming convention of the files is as follows: XXXX_AAY_SZZ

XXXX: 4 digit identifier for the user in the database

AA: finger identifier, it can take values: LI (Left Index), LM (Left Middle), RI (Right Index), RM (Right middle)

Y: sample number, with values 0 to 4

ZZ: acquisition speed, it can take values 10, 30 or 50 mm/sec

 

With the data files we also provide a series of example MATLAB scripts to visualise the 3D fingerprints:

readplytomatrix.m

showFilesPLYformat.m

showFilesRaw.m

showFilesSegmented.m

 

We cannot guarantee the correct functioning of these scripts depending on the MATLAB version you are running.

 

Two videos of the 3D fingerprint scanner can be checked at:

https://www.youtube.com/watch?v=XfbumvzXxnU

https://www.youtube.com/watch?v=2U6fqPIWzMg&t

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This dataset is created with the usage of Galvanic Skin Response Sensor and Electrocardiogram sensor of MySignals Healthcare Toolkit. MySignals toolkit consists of the Arduino Uno board and different sensor ports. The sensors were connected to the different ports of the hardware kit which was controlled by Arduino SDK.

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The dataset consists of echo data collected at the Matre research station (61°N) of the Institute of Marine Research (IMR), Norway. Six square sea cages (12 × 12 m and 15 m depth; approximately 2000 m^3) were used. The fish's vertical distribution and density were observed continuously by a PC-based echo integration system (CageEye MK IV, software version 1.1.1., CageEye AS, Steinkjer, Norway) connected to an upward facing transducer which multiplexes between 50 kHz (42° acoustic beam angle) and 200 kHz (14° beam angle).

Instructions: 

The 1-6 are named 15.1-15.6 respectively. There are some header columns indicating date and time, which can be removed. The depth is along the x-axis in the .csv files, thus the data need to be rotated to get a proper visualization.

1. Unzip data to a folder

2. Import data using pandas (python) or equivalent.

3. Remove the first header columns.

4. Log scale the data.

5. Rotate the data.

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