Task prioritization is one of the most researched areas in software development. Given the huge amount of papers written on the topic, it might be challenging for IT practitioners to find the most appropriate tools or methods developed to date to deal with this important issue. To overcome this problem, we conducted a systematic literature review. The main goal of this work is to review the current state of research and practice on task prioritization among IT practitioners and to individuate the most effective ranking tools and techniques used in the industry.


We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.



Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.



Copyright (C) 2018 Intel Corporation

SPDX-License-Identifier: BSD-3-Clause



Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.


The paper and the dataset can be downloaded from:



To cite the dataset and code, or for further details, please use:

Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019


For questions/comments contact: 






The zip file contains the following files:

1) This README.txt file.

2) LICENSE.txt file.

3) RTT_data.csv - the dataset of FTM transactions

4) Helper Matlab files:

O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.

O PlotFTMchannel.m - plots the channels of a single FTM transaction.

O PlotFTMpositions.m - plots user and Access Point (AP) positions.

O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.

O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.

O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.



Running the Matlab example:


In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.



Contents of the dataset:


The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.

The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')

Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):

O column 1 - Timestamp of each measurement (sec)

O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)

O column 5 - Range, as estimated by the devices in real time (meters)

O columns 6 to 8 - Access Point (AP) position (meters, in local frame)

O column 9 - AP index/number, according the convention of the ION ITM 2019 paper

O column 10 - Ground truth range between the AP and client (meters)

O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)

O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:

O columns 12 to 125  - Complex channel estimates for first antenna from the client device

O columns 126 to 239 - Complex channel estimates for second antenna from the client device

O columns 240 to 353 - Complex channel estimates for first antenna from the AP device

O columns 354 to 467 - Complex channel estimates for second antenna from the AP device

The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).

Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.



After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms.


To extract the dataset, please unzip the main file 'Post-hurricane.zip'. There will be 4 folders inside:

  1. train_another : the training data; 5000 images of each class
  2. validation_another: the validation data; 1000 images of each class
  3. test_another : the unbalanced test data; 8000/1000 images of damaged/undamaged classes
  4. test : the balanced test data; 1000 images of each class

All images are in JPEG format, the class label is the name of the super folder containing the images