Usman Shuja

The IPL and the vast Indian consumer base are attracting other sports owners, data analysts, and lots of others interested in making money

This is the third wave of enthusiasm around AI, a field of study that has been around for more than 60 years. AI 3.0 promises to get us closer to machines with human-like intelligence and change every aspect of our lives including the way we play, operate and consume sports. Is the promise of AI 3.0 real?
In an AI 3.0 era where machines would form opinions and learn continuously, how would it impact the role of humans in the sports industry? By discussing use cases where AI can enrich… Show more

This is the third wave of enthusiasm around AI, a field of study that has been around for more than 60 years. AI 3.0 promises to get us closer to machines with human-like intelligence and change every aspect of our lives including the way we play, operate and consume sports. Is the promise of AI 3.0 real?
In an AI 3.0 era where machines would form opinions and learn continuously, how would it impact the role of humans in the sports industry? By discussing use cases where AI can enrich analysis and create opportunities for competitive advantage - such as improving "situational awareness" and player performance sustainability - we will examine what makes AI 3.0 special and how sports organizations need to think about maximizing the potential opportunity. Show less

Cricket has not been able to capture USA's attention the way other amateur sports like lacrosse and rugby have done. What can the USACA do to try and change that?

How Cognitive Computing is addressing cyber security challenges

This paper suggests a receiver-based scheduler to limit receiving data from other services. We implement Start-Time Fair Queuing in Active Names to schedule incoming data and examine its performance. The fundamental challenge lies in overcoming the difficulties to control receiving data. It is relatively easy to schedule sends, as the sender can control its buffer. But in receiving we have to limit reception from other sources to allow fair allocation to all the processes. Data reception can be… Show more

This paper suggests a receiver-based scheduler to limit receiving data from other services. We implement Start-Time Fair Queuing in Active Names to schedule incoming data and examine its performance. The fundamental challenge lies in overcoming the difficulties to control receiving data. It is relatively easy to schedule sends, as the sender can control its buffer. But in receiving we have to limit reception from other sources to allow fair allocation to all the processes. Data reception can be controlled at three levels a) sender, b) router and c) receiver level. Under normal circumstances all these options can be employed but the problem arises when the sender is un trusted as it can consume bandwidth and cause denial of service attacks. Even if the intent of the sender is not to consume the bandwidth, the nature of the network traffic may cause it to be bursty. Unpredictable behavior of bursty senders makes the network resource management harder. We have built a receiver-based scheduler that distributes bandwidth fairly using Start Time Fair Queuing scheduler to schedule receiving data from steady senders and achieves up to 92% efficiency under bursty conditions. Show less