Information warfare American style: the battle for page numbers, real time event data and other factual information. Recent legal developments

Author(s):  
T.A. Lipinski
Author(s):  
Sara M.T. Polo

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.


2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.


2021 ◽  
Author(s):  
Maneesha Vinodini Ramesh ◽  
Ramesh Guntha ◽  
Christian Arnhardt ◽  
Gargi Singh ◽  
Viswanathan Kr ◽  
...  

<p>Monsoons are characterised by the widespread occurrence of  landslides. Tracking each landslide event, developing early warning thresholds, understanding triggers, and initiating disaster rescue and relief efforts are complex for researchers and administration. The ever increasing landslides demand real-time data collection of events to enhance disaster management. In this work we designed and developed a dedicated crowd sourced mobile application, for systematic way of collection, validation, summarization, and dissemination of landslide data in real-time. This unique design of mobile app uses a scalable real-time data collection methodology for tracking landslide events through citizen science, and is available on Google Play Store for free, and at http://landslides.amrita.edu, with software conceived and developed by Amrita University in the context of the UK NERC/FCDO funded LANDSLIP research project (http://www.landslip.org/). This work implemented a structured database that integrates heterogeneous data such as text, numerical, GPS location, landmarks, and images. This methodology enables real-time tracking of landslides utilizing the details such as GPS location, date & time of occurrence, images, type, material, size, impact, area, geology, geomorphology, and comments in real-time. The mobile application has been uniquely designed to avoid missing landslide events and to handle the tradeoff between real-time spatial data collection without compromising the reliability of the data.  To achieve this a multi level user account was created based on their expert levels such as Tracker, Investigator, Expert.  A basic tracking form is presented for the Tracker level, and an extensive form is presented to the Expert level. The reliability of landslide data enhances as the user level increases from Tracker to Expert. Unique UI designs have been utilized to capture, and track the events. The tracking interface is divided into multiple screens; the main screen captures the landslide location through GPS enabled map interface and captures the date/time of the occurrence. Three additional screens capture images, additional details and comments. The 40 questions for landslide event collection used by the Geological Survey of India has been adapted through the collaborative effort of LANDSLIP partners to collect the additional details. The submitted landslides are immediately available for all users to view. The User can view entered landslides through the landslide image listing, Google maps interface, or tabular listing. The landslides can be filtered by date/time and other parameters. The mobile app is designed to be intuitive and fast, and aims to increase awareness about landslide risk through the integrated short documents, and videos. It has guidelines for safety, capturing images, mapping, and choosing the data from the multiple options. The uniqueness of the proposed methodology is that it enhances community participation, integrates event data collection, event data organizing, spatial and temporal summarization, and validation of landslide events and the impact. It pinpoints, maps and alerts real-time landslide events to initiate right disaster management activities to reduce the risk level. The Landslide tracker app was released during the 2020 monsoon season, and more than 250 landslides were recorded through the app.</p>


1999 ◽  
Vol 18 (1) ◽  
pp. 10-19 ◽  
Author(s):  
T.A. Lipinski

2003 ◽  
Vol 1855 (1) ◽  
pp. 168-175 ◽  
Author(s):  
Xiaoping Zhang ◽  
Yinhai Wang ◽  
Nancy L. Nihan ◽  
Mark E. Hallenbeck

Typical freeway inductive loop detection systems, under normal operation, aggregate individual loop-detector actuations sampled at 60 Hz into 20-s or 30-s averages of velocity, flow, and lane-occupancy measurements. While such aggregations are appropriate for serving as inputs to control system algorithms, and they save disk space for archiving loop data, a large amount of useful data regarding individual vehicles is lost. For single-loop detectors, the lost information includes individual vehicle arrival, departure, and presence times. For speed traps, the lost information also includes the calculated individual vehicle speed and length. Yet this information about individual vehicles is desirable to transportation researchers and planners. The unavailability of this information makes in-depth investigation of detector errors difficult or even impossible. A system for collecting detector event data is proposed. This system can sample loop actuations with sampling rates of 60 Hz or higher and then save, process, and present the collected event data in real time without interfering with the detector controller’s normal operation. A stand-alone Windows program was developed for performing real-time high-frequency loop event data collection. A system reliability test and field application indicate that the system can collect realtime detector event data at a high sampling rate (60 Hz or higher). Additionally, this system makes real-time loop data quality evaluation, loop malfunction identification, and loop error correction feasible.


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