Data Handling

Purpose -This paper aims to present the semantic content identifier (SCI), a permanent identifier, computed through a linear-time onion-peeling algorithm that enables the extraction of semantic features from a text, and the integration of this information within the permanent identifier. Design/methodology/approach -The authors employ SCI to propose a mechanism for simultaneously checking the authenticity and degrees of similarity between different information objects, and present an empirical investigation of the method. A management scenario for the control of the authentication process and the detection of the degree of violation of documents is proposed. Findings -Such a mechanism could be adopted as a component of libraries' strategy for the protection of the copyrights for documents published on the web. Practical implications -The use of the proposed numeric code can be utilised efficiently as a constituent part of the digital object identifier (DOI) system, making its computation more efficient and meaningful. Originality/value -The identifier proposed in the paper can result in a more efficient index for identifying and retrieving objects in a digital library, as well as online repositories and commercial applications that can handle information retrieval requests more effectively.

The New Horizons spacecraft was launched on January 19, 2006. The spacecraft was designed to provide a platform for the seven instruments designated by the science team to collect and return data from Pluto in 2015 that would meet the requirements established by the National Aeronautics and Space Administration (NASA) Announcement of Opportunity AO-OSS-01. The design drew on heritage from previous missions developed at The Johns Hopkins University Applied Physics Laboratory (APL) and other NASA missions such as Ulysses. The trajectory design imposed constraints on mass and structural strength to meet the high launch acceleration consistent with meeting the AO requirement of returning data prior to the year 2020. The spacecraft subsystems were designed to meet tight resource allocations (mass and power) yet provide the necessary control and data handling finesse to support data collection and return when the one way light time during the Pluto fly-by is 4.5 hours. Missions to the outer regions of the solar system (where the solar irradiance is 1/1000 of the level near the Earth) require a Radioisotope Thermoelectric Generator (RTG) to supply electrical power. One RTG was available for use by New Horizons. To accommodate this constraint, the spacecraft electronics were designed to operate on less than 200 W. The travel time to Pluto put additional demands on system reliability. Only after a flight time of approximately ten years would the desired data be collected and returned to Earth. This represents the longest flight duration prior to the return of primary science data for any mission by NASA. The spacecraft system architecture provides sufficient redundancy to meet this requirement with a probability of mission success of greater than 0.85. The spacecraft is now on its way to Pluto with an arrival date of July 14, 2015. Initial in-flight tests have verified that the spacecraft will meet the design requirements.

COVID-19's outbreak affected and compelled people from all walks of life to self-quarantine in their houses in order to prevent the virus from spreading. As a result of adhering to the exceedingly strict guideline, many people developed mental illnesses. Because the educational institution was closed at the time, students remained at home and practiced self-quarantine. As a result, it is necessary to identify the students who developed mental illnesses at that time. To develop AiPsych, a mobile application-based artificial psychiatrist, we train supervised and deep learning algorithms to predict the mental illness of students during the COVID-19 situation. Our experiment reveals that supervised learning outperforms deep learning, with a 97% accuracy of the Support Vector Machine (SVM) for mental illness prediction. Random Forest (RF) achieves the best accuracy of 91% for the recovery suggestion prediction. Our android application can be used by parents, educational institutes, or the government to get the predicted result of a student's mental illness status and take proper measures to overcome the situation.

Twitter demonstrated its value as a viable substitute to traditional communication channels during the recent disasters. However, little is written about Twitter in government for an early disaster warning system. In this exploratory empirical research, we aim to address the question: How does the government use Twitter to inform the public about disaster hazards and vulnerability? Case study and tweets content analysis are conducted on Indonesia's Twitter early tsunami warning system to answer the question in the context of the three earthquakes occurred off the west coast of Sumatra during the period of 2010-2012. Data are collected from egovernment websites of agencies involved in disaster preparedness and response. This research concludes that the Twitter-based warning system demonstrated its value as a viable complement to Indonesia's InaTEWS a comprehensive disaster information management system for governmentsby informing the public and creating public value through its communication speed, reach and information quality.

Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm starts by reading plain document images, simulates forging simulation tasks on five different countries' passports, driving licences and VISA stickers. Then it keeps the bounding boxes as a track of the forged characters as a labeling process. Furthermore, considering the real world scenario, we performed the selected data augmentation accordingly. Regarding the stats of datasets, each dataset consists of 15000 images having size of 950 x 550 of each. For further research purpose we release our algorithm code 1 and, datasets i.

A systems analysis and design of information requirements for an experimental farm is presented. This study was carried out on the university farm (UF) at the University of Copenhagen in Denmark. The UF has several farm sites, many employees and clients, and collects data on the spatial variability of sites for site-specific management, and is responsible for running field trials. Soft and hard system analyses were performed to better understand the information needs and design an information system for the UF. Soft systems methodology was used to analyse the human activities and to identify user requirements, while a hard systems methodology was used to structure the data handling inside the farm office. The resulting information management system (IMS) includes modules for storage, processing and presentation of spatio-temporal data for research trials and site-specific management. A GIS-based farm IMS including the necessary interfaces was implemented and validated by the UF manager and staff. Limitations and constraints to the full implementation of the IMS in an experimental farm are also discussed.