This document contains personal and professional details of Jitendra Kumar Sharma. It summarizes his education qualifications including a M.S. in Multimedia & Data Management from Ecole Polytech Nantes in France and a B.Tech. in Computer Science & Engineering. It also outlines his professional experience as an Assistant Professor, Research Associate, Software Engineer intern, and Web Developer intern. His skills include programming languages like C, C++, Java, ASP.net, Python, and R as well as tools like NetBeans IDE, Matlab IDE, PostgreSQL, and Linux/Unix.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. Per. : Village- Naya Gaon
Bolka, Teh- Rajgarh
(Rajasthan)
Current : Kodugedi,
Bangalore
+91 (0)9166019125
Date of Birth:
October 28, 1991
SHARMA JITENDRA KUMAR
http://jitendraksharma.strikingly.com/
jitendra-kumar.sharma@etu.univ-nantes.fr
jitudausa.sharma50@gmail.com
PROFESSIONAL OBJECTIVE
Associate myself with a progressive and well rooted organisation, which provides me a platform to unleash my competencies, creativity
and take my career to higher avenues of success leading to professional and personal development along with value addition to the
organisation I work for.
Professional Experience: One Year professional Experience As Assistant Professor/Research Associate in BK Birla Institute of
Engineering & Technology/ Aditya Birla Training and Research Centre (2013-2014). Five Months Research Experience at laboratory LINA
(UMR CNRS 6241) Equipe Duke (Data User Knowledge) Polytech Nantes, France (Apr/2015-Aug/2015)
EDUCATION
Nantes, France Ecole Polytech Nantes (France) 2013 – 2015
M.S in Multimedia & Data Management
Specialization: Data management, Data Mining and Image processing
Graduate Coursework: Software Foundations; Algorithms; Artificial Intelligence; Computer Vision; BigData Management;
Machine Learning.
Pilani, India B.K Birla Institute of Engineering & Technology 2009 – 2013
Bachelors of Technology in Computer Science & Engineering Major CGPA: 6.5/10
Undergraduate Coursework: Operating Systems; Programming Languages; Database
PROFESSIONAL EXPERIENCE & ACADEMICS PROJECTS
Data Engineer, Intern LINA Lab, Nantes (France) April 2015–Aug’2015
Build a tool for Data Profiling, Risk evaluation and Reporting for sanitized data sets
Platform iPython (pandas).
Evaluation of risk and utility for small and large scale data sets.
Software Engineer, Intern ABTRC, Pilani (India) Sep’2013 – July 2014
1. E-student attendance system using image processing
The idea is to use of automatic system.
The working principle of system is, first create a database of students eyes then capture the real time image of eyes then
compare it with data base.
2. Real Time Image Processing for object tracking
The idea is to Increase the use of Surveillance cameras, which will improve the security system.
The working principle of Object tracking is , first capture the image in real time (frame by frame) and then capture each and
every movement in that particular area.
Web Developer, Intern CMC Ltd., Jaipur (India) June 2012– July 2012
Hospital management System
Software-Coding part is in ASP.net programming and SQL for database.
LANGUAGES AND TECHNOLOGIES
Languages C, C++, Java basics, ASP.net, MATLAB , basics of Open CV library, Python, R language.
Application S/W NetBeans IDE, Matlab IDE, QualNet Simulator, Python , Tableau basic knowledge, Linux, Unix.
D.B.M.S. MS Sql Server, SQL Access, PostGreSql
Languages French (A1), English, Hindi
Extra Curricular & Design a model for pure water extraction from wastewater, Attend International Conference
Social Activities on Communication System, Active member of The Institution of Engineers (India).