This dataset includes the measurements of a simulated vehicle inside a Gazebo simulation using different sensors: a simulated UWB tag, a IMU and a PX4Flow. 


Please, if you use the datase, put a reference to our article: 


Barral, V., Suárez-Casal, P., Escudero, C. J., & García-Naya, J. A. (2019). Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments. Electronics8(10), 1152.


The dataset includes several .mat files for each scenario. Each file contains a ROS set of topics. To read each topic is needed the Matlab Robotics System Toolbox. An example script is added.


AllMeasurementsScenarioXX.mat contains the sensor data and position estimates on scenario XX.

The available topics are:

- /gtec/gazebo/pos  : Ground truth positions

- /gtec/kfpos_uwb : location estimates, only UWB

- /gtec/kfpos_uwb_imu : location estimates, UWB + IMU

- /gtec/kfpos_uwb_imu_px4: location estimates, UWB + IMU + PX4 FLOW

- /gtec/gazebo/imu : IMU data

- /gtec/gazebo/uwb/ranging/0: UWB ranging data

- /gtec/gazebo/px4flow: PX4Flow


AnchorsScenarioXX.mat contains the positions of the UWB anchors in scenario XX.

The available topics are:


/gtec/gazebo/uwb/anchors/0 : The positions of each anchor.


ScenarioXXWalls.mat includes the position of the walls in the scenario XX.

The available topics are:


/tf : The positions of each model in the scenario.



Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images.


In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels. 



Documentation will be available soon.


The Data Fusion Contest 2016: Goals and Organization

The 2016 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.

New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification:



After unzip, each directory contains:

  • original GeoTiff for panchromatic (VHR) and multispectral (4bands) images,

  • quick-view image for both in png format,

  • capture parameters (RPC file).



This dataset page is currently being updated. The tweets collected by the model deployed at are shared here. However, because of COVID-19, all computing resources I have are being used for a dedicated collection of the tweets related to the pandemic. You can go through the following datasets to access those tweets:


We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)

It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.


To facilitate use of some deep learning softwares, a folder tree with relative symbolic link (thus avoiding extra space) will gather all the sequences in three folders : | |— image |        | — sequenceName0_imageNumber_timestamp0.jpg |        | — sequenceName0_imageNumber_timestamp1.jpg |        | — sequenceName0_imageNumber_timestamp2.jpg |        | — sequenceName0_imageNumber_timestamp3.jpg |        | — … | |— depth_8bits |        | — sequenceName0_imageNumber_timestamp0.png |        | — sequenceName0_imageNumber_timestamp1.png |        | — sequenceName0_imageNumber_timestamp2.png |        | — sequenceName0_imageNumber_timestamp3.png |        | — … | |— annotations |        | — sequenceName0_imageNumber_timestamp0.xml |        | — sequenceName0_imageNumber_timestamp1.xml |        | — sequenceName0_imageNumber_timestamp2.xml |        | — sequenceName0_imageNumber_timestamp3.xml |        | — … |


This dataset is part of our research on malware detection and classification using Deep Learning. It contains 42,797 malware API call sequences and 1,079 goodware API call sequences. Each API call sequence is composed of the first 100 non-repeated consecutive API calls associated with the parent process, extracted from the 'calls' elements of Cuckoo Sandbox reports.



Column name: hash
Description: MD5 hash of the example
Type: 32 bytes string

Column name: t_0 ... t_99
Description: API call
Type: Integer (0-306)

Column name: malware
Description: Class
Type: Integer: 0 (Goodware) or 1 (Malware)

API Calls: ['NtOpenThread', 'ExitWindowsEx', 'FindResourceW', 'CryptExportKey', 'CreateRemoteThreadEx', 'MessageBoxTimeoutW', 'InternetCrackUrlW', 'StartServiceW', 'GetFileSize', 'GetVolumeNameForVolumeMountPointW', 'GetFileInformationByHandle', 'CryptAcquireContextW', 'RtlDecompressBuffer', 'SetWindowsHookExA', 'RegSetValueExW', 'LookupAccountSidW', 'SetUnhandledExceptionFilter', 'InternetConnectA', 'GetComputerNameW', 'RegEnumValueA', 'NtOpenFile', 'NtSaveKeyEx', 'HttpOpenRequestA', 'recv', 'GetFileSizeEx', 'LoadStringW', 'SetInformationJobObject', 'WSAConnect', 'CryptDecrypt', 'GetTimeZoneInformation', 'InternetOpenW', 'CoInitializeEx', 'CryptGenKey', 'GetAsyncKeyState', 'NtQueryInformationFile', 'GetSystemMetrics', 'NtDeleteValueKey', 'NtOpenKeyEx', 'sendto', 'IsDebuggerPresent', 'RegQueryInfoKeyW', 'NetShareEnum', 'InternetOpenUrlW', 'WSASocketA', 'CopyFileExW', 'connect', 'ShellExecuteExW', 'SearchPathW', 'GetUserNameA', 'InternetOpenUrlA', 'LdrUnloadDll', 'EnumServicesStatusW', 'EnumServicesStatusA', 'WSASend', 'CopyFileW', 'NtDeleteFile', 'CreateActCtxW', 'timeGetTime', 'MessageBoxTimeoutA', 'CreateServiceA', 'FindResourceExW', 'WSAAccept', 'InternetConnectW', 'HttpSendRequestA', 'GetVolumePathNameW', 'RegCloseKey', 'InternetGetConnectedStateExW', 'GetAdaptersInfo', 'shutdown', 'NtQueryMultipleValueKey', 'NtQueryKey', 'GetSystemWindowsDirectoryW', 'GlobalMemoryStatusEx', 'GetFileAttributesExW', 'OpenServiceW', 'getsockname', 'LoadStringA', 'UnhookWindowsHookEx', 'NtCreateUserProcess', 'Process32NextW', 'CreateThread', 'LoadResource', 'GetSystemTimeAsFileTime', 'SetStdHandle', 'CoCreateInstanceEx', 'GetSystemDirectoryA', 'NtCreateMutant', 'RegCreateKeyExW', 'IWbemServices_ExecQuery', 'NtDuplicateObject', 'Thread32First', 'OpenSCManagerW', 'CreateServiceW', 'GetFileType', 'MoveFileWithProgressW', 'NtDeviceIoControlFile', 'GetFileInformationByHandleEx', 'CopyFileA', 'NtLoadKey', 'GetNativeSystemInfo', 'NtOpenProcess', 'CryptUnprotectMemory', 'InternetWriteFile', 'ReadProcessMemory', 'gethostbyname', 'WSASendTo', 'NtOpenSection', 'listen', 'WSAStartup', 'socket', 'OleInitialize', 'FindResourceA', 'RegOpenKeyExA', 'RegEnumKeyExA', 'NtQueryDirectoryFile', 'CertOpenSystemStoreW', 'ControlService', 'LdrGetProcedureAddress', 'GlobalMemoryStatus', 'NtSetInformationFile', 'OutputDebugStringA', 'GetAdaptersAddresses', 'CoInitializeSecurity', 'RegQueryValueExA', 'NtQueryFullAttributesFile', 'DeviceIoControl', '__anomaly__', 'DeleteFileW', 'GetShortPathNameW', 'NtGetContextThread', 'GetKeyboardState', 'RemoveDirectoryA', 'InternetSetStatusCallback', 'NtResumeThread', 'SetFileInformationByHandle', 'NtCreateSection', 'NtQueueApcThread', 'accept', 'DecryptMessage', 'GetUserNameExW', 'SizeofResource', 'RegQueryValueExW', 'SetWindowsHookExW', 'HttpOpenRequestW', 'CreateDirectoryW', 'InternetOpenA', 'GetFileVersionInfoExW', 'FindWindowA', 'closesocket', 'RtlAddVectoredExceptionHandler', 'IWbemServices_ExecMethod', 'GetDiskFreeSpaceExW', 'TaskDialog', 'WriteConsoleW', 'CryptEncrypt', 'WSARecvFrom', 'NtOpenMutant', 'CoGetClassObject', 'NtQueryValueKey', 'NtDelayExecution', 'select', 'HttpQueryInfoA', 'GetVolumePathNamesForVolumeNameW', 'RegDeleteValueW', 'InternetCrackUrlA', 'OpenServiceA', 'InternetSetOptionA', 'CreateDirectoryExW', 'bind', 'NtShutdownSystem', 'DeleteUrlCacheEntryA', 'NtMapViewOfSection', 'LdrGetDllHandle', 'NtCreateKey', 'GetKeyState', 'CreateRemoteThread', 'NtEnumerateValueKey', 'SetFileAttributesW', 'NtUnmapViewOfSection', 'RegDeleteValueA', 'CreateJobObjectW', 'send', 'NtDeleteKey', 'SetEndOfFile', 'GetUserNameExA', 'GetComputerNameA', 'URLDownloadToFileW', 'NtFreeVirtualMemory', 'recvfrom', 'NtUnloadDriver', 'NtTerminateThread', 'CryptUnprotectData', 'NtCreateThreadEx', 'DeleteService', 'GetFileAttributesW', 'GetFileVersionInfoSizeExW', 'OpenSCManagerA', 'WriteProcessMemory', 'GetSystemInfo', 'SetFilePointer', 'Module32FirstW', 'ioctlsocket', 'RegEnumKeyW', 'RtlCompressBuffer', 'SendNotifyMessageW', 'GetAddrInfoW', 'CryptProtectData', 'Thread32Next', 'NtAllocateVirtualMemory', 'RegEnumKeyExW', 'RegSetValueExA', 'DrawTextExA', 'CreateToolhelp32Snapshot', 'FindWindowW', 'CoUninitialize', 'NtClose', 'WSARecv', 'CertOpenStore', 'InternetGetConnectedState', 'RtlAddVectoredContinueHandler', 'RegDeleteKeyW', 'SHGetSpecialFolderLocation', 'CreateProcessInternalW', 'NtCreateDirectoryObject', 'EnumWindows', 'DrawTextExW', 'RegEnumValueW', 'SendNotifyMessageA', 'NtProtectVirtualMemory', 'NetUserGetLocalGroups', 'GetUserNameW', 'WSASocketW', 'getaddrinfo', 'AssignProcessToJobObject', 'SetFileTime', 'WriteConsoleA', 'CryptDecodeObjectEx', 'EncryptMessage', 'system', 'NtSetContextThread', 'LdrLoadDll', 'InternetGetConnectedStateExA', 'RtlCreateUserThread', 'GetCursorPos', 'Module32NextW', 'RegCreateKeyExA', 'NtLoadDriver', 'NetUserGetInfo', 'SHGetFolderPathW', 'GetBestInterfaceEx', 'CertControlStore', 'StartServiceA', 'NtWriteFile', 'Process32FirstW', 'NtReadVirtualMemory', 'GetDiskFreeSpaceW', 'GetFileVersionInfoW', 'FindFirstFileExW', 'FindWindowExW', 'GetSystemWindowsDirectoryA', 'RegOpenKeyExW', 'CoCreateInstance', 'NtQuerySystemInformation', 'LookupPrivilegeValueW', 'NtReadFile', 'ReadCabinetState', 'GetForegroundWindow', 'InternetCloseHandle', 'FindWindowExA', 'ObtainUserAgentString', 'CryptCreateHash', 'GetTempPathW', 'CryptProtectMemory', 'NetGetJoinInformation', 'NtOpenKey', 'GetSystemDirectoryW', 'DnsQuery_A', 'RegQueryInfoKeyA', 'NtEnumerateKey', 'RegisterHotKey', 'RemoveDirectoryW', 'FindFirstFileExA', 'CertOpenSystemStoreA', 'NtTerminateProcess', 'NtSetValueKey', 'CryptAcquireContextA', 'SetErrorMode', 'UuidCreate', 'RtlRemoveVectoredExceptionHandler', 'RegDeleteKeyA', 'setsockopt', 'FindResourceExA', 'NtSuspendThread', 'GetFileVersionInfoSizeW', 'NtOpenDirectoryObject', 'InternetQueryOptionA', 'InternetReadFile', 'NtCreateFile', 'NtQueryAttributesFile', 'HttpSendRequestW', 'CryptHashMessage', 'CryptHashData', 'NtWriteVirtualMemory', 'SetFilePointerEx', 'CertCreateCertificateContext', 'DeleteUrlCacheEntryW', '__exception__']


We would like to thank: Cuckoo Sandbox for developing such an amazing dynamic analysis environment!
VirusShare! Because sharing is caring!
Universidade Nove de Julho for supporting this research.
Coordination for the Improvement of Higher Education Personnel (CAPES) for supporting this research.


"Oliveira, Angelo; Sassi, Renato José (2019): Behavioral Malware Detection Using Deep Graph Convolutional Neural Networks. TechRxiv. Preprint." at Please feel free to contact me for any further information.


Our efforts are made on one-shot voice conversion where the target speaker is unseen in training dataset or both source and target speakers are unseen in the training dataset. In our work, StarGAN is employed to carry out voice conversation between speakers. An embedding vector is used to represent speaker ID. This work relies on two datasets in English and one dataset in Chinese, involving 38 speakers. A user study is conducted to validate our framework in terms of reconstruction quality and conversation quality.


This is the supporting content for my ICASSP 2020 paper.

Paper number: 5581.


The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits.


#Basic Intructions for usage

Make sure you have the following folder structure in the data directory after you unzip the file:


├── splits

├── test_once

│   ├── test1_labels.npy

│   ├── test1_seismic.npy

│   ├── test2_labels.npy

│   └── test2_seismic.npy

└── train

    ├── train_labels.npy

    └── train_seismic.npy

The train and test data are in NumPy .npy format ideally suited for Python. You can open these file in Python as such: 

import numpy as np

train_seismic = np.load('data/train/train_seismic.npy')

Make sure the testing data is only used once after all models are trained. Using the test set multiple times makes it a validation set.

We also provide fault planes, and the raw horizons that were used to generate the data volumes in addition to the processed data volumes before splitting to training and testing.

# References:

1- Netherlands Offshore F3 block. [Online]. Available: OffshoreF3BlockComplete4GB

2- Alaudah, Yazeed, et al. "A machine learning benchmark for facies classification." Interpretation 7.3 (2019): 1-51.



A well-known publicly available database namely UniProt was the main source for collection beta-lactamase and non-beta-lactamase protein sequences. To obtain relevant positive sequences ‘beta-lactamase’ was used as a keyword. The dataset was meticulously collected by excluding ambiguous sequences, only those sequences were selected which were not annotated with dubious words like potential, by similarity or probable. Moreover, the sequence should be a complete sequence and hence should not be annotated with words like fragment. beta-lactamase protein sequences as well.