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Register now: Webinar on the Importance of Data cleaning

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Date: 05th August 2021
Time:  10 am (UTC)
Duration: 40 min session and 20 min Question Answers (Total 1 hour)

Registration link: https://attendee.gotowebinar.com/register/2137644534742391819

Data cleaning might seem dull and uninteresting, but it’s one of the most important tasks you would have to do as a data science professional. Correcting or removing “dirty data” improves the reliability and value of response data for better decision-making. Data cleaning involves the detection and removal (or correction) of errors and inconsistencies in a data set due to the corruption/irrelevance or inaccurate entry of the data.  Incomplete, inaccurate or irrelevant data is identified and then either replaced, modified or deleted.

Incorrect or inconsistent data can create a number of problems which lead to the drawing of false conclusions.  Therefore, data cleaning can be an important element in some data analysis situations.  Having wrong or bad quality data can be detrimental to your processes and analysis. Poor data can cause a stellar algorithm to fail. However, data cleaning is not without risks and problems including the loss of important information or valid data.

Data cleansing is also important because it improves your data quality and in doing so, increases overall productivity. When you clean your data, all outdated or incorrect information is gone – leaving you with the highest quality information. This ensures you do not have to wade through countless outdated documents and allows you to make the most of your project hours

Name of the Speaker: Simisani Ndaba
Designation: Teaching Assistant
Affiliation: University of Botswana

Simisani has a history of working in the higher education industry having been working at the Department of Computer Science at the University of Botswana as a Teaching Assistant since 2016. She graduated with her Masters of Science in Computer Information Systems where her research work was based on Information Retrieval in Authorship Identification using authors’ writing styles using PAN at CLEF. PAN is a series of scientific events and shared tasks on digital text forensics and stylometry. Prior to that, she worked as a Business Analyst at the Gauteng Department of Education working on data management and business intelligence in South Africa. She also holds a Bachelor’s degree in Business Information Systems and is due to complete a Post Graduate Diploma in Education, a teacher/trainer qualification in October 2021. She is part of the Ladies in R Botswana based in the University of Botswana and is an assistant in Health Informatics Africa.

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Source: https://codata.org/register-now-webinar-on-the-importance-of-data-cleaning/

Big Data

If you did not already know

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Data Archaeology google


Data archaeology refers to the art and science of recovering computer data encoded and/or encrypted in now obsolete media or formats. Data archaeology can also refer to recovering information from damaged electronic formats after natural or man made disasters. …

UR-FUNNY google


Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research. …

Firebreak Decision Problem google


Suppose we have a network that is represented by a graph $G$. Potentially a fire (or other type of contagion) might erupt at some vertex of $G$. We are able to respond to this outbreak by establishing a firebreak at $k$ other vertices of $G$, so that the fire cannot pass through these fortified vertices. The question that now arises is which $k$ vertices will result in the greatest number of vertices being saved from the fire, assuming that the fire will spread to every vertex that is not fully behind the $k$ vertices of the firebreak. This is the essence of the Firebreak decision problem. …

Stochastic Substitute Training google


It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/07/28/if-you-did-not-already-know-1460/

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Big Data

If you did not already know

Published

on

Data Archaeology google


Data archaeology refers to the art and science of recovering computer data encoded and/or encrypted in now obsolete media or formats. Data archaeology can also refer to recovering information from damaged electronic formats after natural or man made disasters. …

UR-FUNNY google


Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research. …

Firebreak Decision Problem google


Suppose we have a network that is represented by a graph $G$. Potentially a fire (or other type of contagion) might erupt at some vertex of $G$. We are able to respond to this outbreak by establishing a firebreak at $k$ other vertices of $G$, so that the fire cannot pass through these fortified vertices. The question that now arises is which $k$ vertices will result in the greatest number of vertices being saved from the fire, assuming that the fire will spread to every vertex that is not fully behind the $k$ vertices of the firebreak. This is the essence of the Firebreak decision problem. …

Stochastic Substitute Training google


It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/07/28/if-you-did-not-already-know-1460/

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Big Data

Sony’s PS5 outstrips predecessor with 10 million units sold since Nov launch

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By Sam Nussey

TOKYO (Reuters) – Sony Group Corp said on Wednesday its PlayStation 5 (PS5) gaming console has sold more than 10 million units since launching last November, outstripping sales of its predecessor even as the Japanese firm grapples with a global chip shortage.

The PS5, which offers cutting edge graphics and faster loading times than the PS4, is in short supply as the COVID-19 pandemic strains global semiconductor supply chains while demand has risen amid a gaming boom with more people staying indoors.

“We’ve built more PlayStations faster than we ever have before which makes me happy. But on the other hand, we’re some time from being able to meet all the demand that’s out there, which makes me feel bad,” Sony Interactive Entertainment CEO Jim Ryan told Reuters via email.

“Our partners are performing really well for us, but the chip shortage is definitely a challenge that we are all navigating,” Ryan said.

Boosted by exclusive games likes Marvel’s Spider-Man: Miles Morales, which has sold more than 6.5 million copies, PS5 sales have outstripped the PS4.

It took Sony around nine months to sell 10 million units of the PS4, which had a staggered launch. More than 100 million units of the console have been sold since November 2013.

Electronics makers warn of deepening semiconductor shortages, with Apple on Tuesday saying the shortfall is affecting iPhone production.

“Sony’s deep expertise in supply chain management for consumer electronics has enabled it to weather the worst impacts of the pandemic even during the launch of a new product,” said Piers Harding-Rolls, head of games research at Ampere Analysis.

Sony sees demand for the PS5 continuing even as vaccinations spur easing of curbs on going out, Ryan said.

A strong games slate will be crucial to maintain momentum amid competition from Microsoft’s rival Xbox device, analysts say.

Another first-party title for Sony, Ratchet & Clank: Rift Apart, has sold more than 1.1 million copies since its release last month. First-party titles refer to games from companies that are owned by the firm making the console.

The group forecasts PS5 hardware sales of at least 14.8 million units in the year through March.

(Reporting by Sam Nussey; Editing by Himani Sarkar)

Image Credit: Reuters

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://datafloq.com/read/sonys-ps5-outstrips-predecessor-10-million-units-sold-since-nov-launch/16704

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Big Data

U.S. senators urge barring Huawei, ZTE from $1.9 trillion gov’t funding measure

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By David Shepardson

WASHINGTON (Reuters) -Two U.S. senators on Wednesday said they are introducing a measure to prohibit funds in a $1.9 trillion government funding measure from being used to purchase Chinese telecommunications equipment from Huawei, ZTE and other companies deemed U.S. security threats.

Senators Tom Cotton, a Republican, and Mark Warner, a Democrat, said the funds that were approved in March in a law known as the American Rescue Plan should not be used to potentially undermine U.S. telecommunications networks.

“With states across the country mapping out their plans for quality and affordable high-speed internet as a result of historic funding from the American Rescue Plan, we’ve got to make sure no community is sacrificing network security,” said Warner.

Huawei and ZTE did not immediately comment.

“The U.S government must take strong action to cut the Chinese Communist Party out of our networks. Americans deserve both reliable and secure telecommunications technologies,” said Cotton.

Earlier this month, the U.S. Federal Communications Commission (FCC) voted unanimously to finalize a $1.9 billion program to reimburse mostly rural U.S. carriers for removing equipment from telecommunications networks from Chinese companies like Huawei and ZTE.

Last year, the FCC designated Huawei and ZTE as national security threats to communications networks – a declaration that barred U.S. firms from tapping an $8.3 billion government fund to purchase equipment from the companies. The FCC in December adopted rules requiring carriers with ZTE or Huawei equipment to “rip and replace” that equipment.

The FCC in September 2020 estimated it would cost $1.837 billion to remove and replace Huawei and ZTE equipment from networks.

(Reporting by David Shepardson in WashingtonEditing by Jonathan Oatis and Matthew Lewis)

Image Credit: Reuters

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://datafloq.com/read/us-senators-urge-barring-huawei-zte-19-trillion-govt-funding-measure/16703

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