This document describes the details of the BON Egocentric vision dataset. BON denotes the initials of the locations where the dataset was collected; Barcelona (Spain); Oxford (UK); and Nairobi (Kenya). BON comprises first-person video, recorded when subjects were conducting common office activities. The preceding version of this dataset, FPV-O dataset has fewersubjects for only a single location (Barcelona). To develop a location agnostic framework, data from multiple locations and/or office settings is essential.


Instructions are available on the attached document


Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution.


Water consumption. Data recorded between 2017.1.1 and 2019.12.31.


With the motivation of no good data sources available for all diseases (from generic to chronic) and their treatment courses, a new dataset is synthesized by exploring several medical websites and resources. It provides the precaution list corresponding to over 1000+ diaganosis. prec\_t.csv : (did, diagnose, pid) = (Disease identifier, Disease name, treatment course). This dataset can be utilized for many machine learning or deep learning based healthcare applications.


Depressive/Non-depressive tweets  between December 2019 and December 2020 originated largely from India and parts of Indian subcontinent. Sentiment Scores alloted using text blob. Tweets are extracted specifically keeping in mind the top 250 most frequently used negative lexicons and positive lexicons accesed using SentiWord and various research publications.

Tweet Amount : 1.4 Lakhs





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Dataset used in the article "An Ensemble Method for Keystroke Dynamics Authentication in Free-Text Using Word Boundaries". For each user and free-text sample of the companion dataset LSIA, contains a CSV file with the list of words in the sample that survived the filters described in the article, together with the CSV files with training instances for each word. The source data comes from a dataset used in previous studies by the authors. The language of the free-text samples is Spanish.


Dataset asscociated with a paper in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems

"Talk the talk and walk the walk: Dialogue-driven navigation in unknown indoor environments"

If you use this code or data, please cite the above paper.



See the docs directory.


This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R16 standards-complied.


5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).

Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G  NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.



The dataset_[SNR]_[date]_[time].mat contains: 

1) a 4-D matrix, features, representing the feature data, and

2) a structure array, labels, labeling the ground truth of UE positions.

[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.

The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).

The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.

The value of Ng corresponds to the number of UEs in labels.


 Colsed beta test is running.

In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.


In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.


Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.

To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.


 Release note 

2021-07-23 :

1) Recruit participants for colsed beta test.

2021-07-22 :

1)Expend our dataset with more CSI data with low SNR levels noise.

2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Closed beta test will start after suggestion collection.

2021-07-18 :

1)Expend our dataset with more CSI data with different SNR levels noise.

2)Publish map files for Scenario 1 indoor office.




In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing the search range of the decision space will significantly alleviate this puzzle. With this in mind, this paper proposes a fuzzy decision variables framework for large-scale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution.