This profile is of a 18-26 year old single British atheist female who is currently a student, unemployed, or works part-time. She comes from a middle-class background and enjoys a mainstream social life. She spends her time studying, socializing, or working in a mediocre paid job. She is confident and likes to go out dancing and singing at nightclubs and karaoke bars with her friends while keeping up with the latest fashion trends and being conscious of her appearance.
2007 yılında Madrid’de kurulan ve kullanıcılarına cashback, indirim kuponu ve özel fırsatlar sunan İspanya merkezli BeRuby, 14 ülkede faaliyet göstermekte olup Latin Amerika ve Güney Avrupa’da pazar lideridir. 2011 ve 2012 yıllarında İspanya’da “En iyi Eticaret Hizmet Sağlayıcısı” seçilmiştir.
An informative guide on room layouts for your next corporate event. This presentations will aid those with their venue finding efforts and help you to understand the difference between different rooming styles.
For all your event management and venue finding needs please contact In 2 Events at www.in2events.co.uk
2007 yılında Madrid’de kurulan ve kullanıcılarına cashback, indirim kuponu ve özel fırsatlar sunan İspanya merkezli BeRuby, 14 ülkede faaliyet göstermekte olup Latin Amerika ve Güney Avrupa’da pazar lideridir. 2011 ve 2012 yıllarında İspanya’da “En iyi Eticaret Hizmet Sağlayıcısı” seçilmiştir.
An informative guide on room layouts for your next corporate event. This presentations will aid those with their venue finding efforts and help you to understand the difference between different rooming styles.
For all your event management and venue finding needs please contact In 2 Events at www.in2events.co.uk
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.
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. DEMOGRAPHIC PROFILE
Gender: Female
Religion: Atheist
Age group: 18- 26
Socio-economic group: C1 and C2
Occupation: Student, Unemployed, part time work
Social Group: Mainstream; townies
Marital status: Single
Income: None- minimal wage
Nationality: British
3. PSYCHOGRAPHIC PROFILE
A confident individual who spends most of her time either
studying, socializing or working a job at mediocre pay.
She enjoys listening to mainstream music and attending
concerts of popular artists. She is seen as a ‘party girl’
who loves socializing with familiar and new people and is
frequently found at multiple night clubs. She is a common
customer at karaoke bars and is always dancing and
singing. She is often seen in the latest fashion as she is
quite self conscious of her appearance but is not afraid to
speak her mind when necessary.
Likes: Socializing with friends, nights out, shopping for
bargains, bowling, cinema, night clubbing, reading.
