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A TV station was forced to make a public apology after focusing on a fan's cleavage during coverage of a volleyball match between Turkey and the United States.<br/><br/>As defending champions Turkey triumphed in a 3-2 victory over the US, the camera cut to the ecstatic reactions of the crowd, relayed in slow motion for effect.<br/><br/>But it was their focus on one female supporter dressed in Turkey's colors that caused a backlash on social media, as several seconds of the woman jumping in jubilation was deemed too much for the intended audiences.<br/><br/>As the woman was reportedly forced to take her social media channels offline following criticism, Turkish TRT Spor released a statement blaming the coverage on its host broadcaster in the US.<br/><br/>They said an 'unwanted image' had appeared during the broadcast, blaming the 'disturbing situation' on the US. <br/><br/>'This image, which is completely contrary to our publication policy, originates from the US broadcaster, and the discomfort felt by this situation has been conveyed to the other party,' the statement read. <br/><br/>Fans erupted on Monday as Turkey prevailed in a hard-fought tie-breaker to overcome the Americans 3-2 in the FIVB Nations League.<br/><br/>As the crowd celebrated the victory on the pitch, the camera held its gaze for a few moments on the reaction of one fan dressed in all red.<br/><br/>The female supporter jumped on the spot, elated by the victory.<br/><br/>But broadcast in slow motion, critics in Turkey were offended by the woman's dress code.<br/><br/>The woman was sadly targeted by threats and insulting messages after the clip was broadcast, before being pushed to close her accounts, according to Turkish media.<br/><br/>With no mention of the woman or abuse endured, the broadcaster shared a press release on Monday after the game.<br/><br/>'We would like the public to know that the shooting and direction of the match was carried out by the US broadcaster,' the statement read.<br/><br/>They said TRT Spor and all other broadcasters using the US stream showed the images relayed to them 'without the possibility of intervention'.<br/><br/>'Therefore, this image which is completely contrary to our publication policy, originates from the US broadcaster, and the discomfort felt by this situation has been conveyed to the other party.<br/><br/>'TRT, in its capacity and responsibility as Turkey's public broadcaster, will continue to be a strong supporter of Turkish sports,' the statement said, wishing the Turkish team luck in their upcoming matches against Thailand, Dominican Republic, China, and Brazil later this month.<br/><br/>Not all agreed. One user on Twitter wrote: 'They should apologize to the real woman, why did they do it?' <br/><br/>Another said: 'She's happy to leave her alone'<br/><br/>But others thought the channel was right to 'apologise' for offending local sensibilities.
TURKISH TV was forced to apologise after a fan suffered a wardrobe malfunction at a volleyball match.
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Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \
Session 17: Deployment of Machine Learning Models in Open RAN Networks
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All Links: ☠️ More Movie<br/>YouTube :::<br/>https://www.youtube.com/@Bailumylulu<br/> Rumble :::<br/> https://rumble.com/c/c-5999868/videos<br/> BiliBili :::<br/>https://www.bilibili.tv/en/space/2055644159<br/>Dailymotion :::<br/>https://www.dailymotion.com/alexmapeli <br/>Dailymotion :::<br/>https://www.dailymotion.com/MusicLulu<br/><br/>Copyright Disclaimer: - Under section 107 of the copyright Act 1976, allowance is mad for FAIR USE for purpose such a as criticism, comment, news reporting, teaching, scholarship and research. Fair use is a use permitted by copyright statues that might otherwise be infringing. Non- Profit, educational or personal use tips the balance in favor of FAIR USE. <br/>Email:::<br/>alexmapliejack@gmile.com
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