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The Future of Work

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The 1st of May is celebrated as International Labor Day, as it historically marks the relentless struggle of the working class to get the workday reduced to 8 hours and the workweek to 40 hours (Al Jazeera, 2019). The history of International Labor Day is rooted in the struggle for freedom and rights. It was initially called the "day of demonstrations," as peaceful protests for the demand of reducing working hours by workers in Chicago were countered by violence by the state. It also led to the sentencing to death of revolutionary leaders, who were tried only because of their political beliefs, without any evidence linking them to violence. Although this movement for labor rights started in the West, it soon reached other parts of the globe as well, where non-Western countries like India, Bangladesh, and Pakistan also initiated similar demonstrations to support better labor rights and opportunities.


Artificial Intelligence projects bag Rs 723m in PSDP - ThePenPK

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ISLAMABAD: The federal government has allocated Rs 723 million for the promotion of Artificial Intelligence (AI) in the country to cope with the challenges of technological advancement. The Defence Division will spend Rs 300 million on the development of Information and Communication Technology (ICT) and AI-based precision agriculture system. Named Green AI, the system will utilize dual-use aerospace technologies. For the establishment of the Sino-Pak Centre for Artificial Intelligence under the Information Technology and Telecom Division, an estimated Rs 243 million has been allocated. The National Centre of Artificial Intelligence, Islamabad will work under the Higher Education Commission, for which Rs 170 million has been allocated.


Work on Pakistan's first Artificial Intelligence lab under CPEC picks momentum

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ISLAMABAD: Seventy-five percent work of Pakistan first high-standard artificial intelligence laboratory under CPEC at National University of Science and Technology (NUST) has been completed while the equipment installation is almost 100% finished, Gwadar Pro reported on Saturday. At the beginning of this year, the laboratory under CPEC–Qingluan Artificial Intelligence Laboratory was officially established at NUST, with joint efforts of NUST and Guangzhou Institute of Chinese Academy of Sciences. Research, development and customization is currently underway. I would say work is almost finished to 75%." Muhammad Khubaib Shabbir, Deputy Director of China Study Center of NUST told Gwadar Pro. The lab has been put into full use, both students and teaching staff are keen on researching Pattern and Facial Recognition algorithms, the reporter learned. "Currently, Cogniser-V1 intelligent video analysis project-a pilot project with the Government of Pakistan, and a commercial project, namely GymBot are the main projects that are under development." "Ideally, Cognizer-V1 is one of the most sophisticated surveillance equipment, which has the capability of converting ordinary cameras and surveillance equipment into a Smart Equipment, using AI and Computer Vision Algorithms." "To put it simple, the Cognizer-V1 has the ability to sense the people who are lurking around in certain areas and generate warnings, regarding dangerous behavioral patterns such as suicide, or other suspicious activities." In the case of Pakistan, the country is blessed with a large number of artificial intelligence application scenarios and a huge market, thanks to its world's 6th largest population. Moreover, the country is never short on talents. However, challenges lie in the commercialization of scientific achievements– an important step which can be viewed as one of the sources for innovation. Due to the backward industrial conditions and obstruction of international exchanges during the epidemic, the progress of commercialization in Pakistani scientific research institutes has been extremely slow. "Our other key project, 'GymBot', can be a perfect example of science commercialization.


75pc work on artificial intelligence lab at NUST completed

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Islamabad: Seventy-five per cent work of Pakistan first high-standard artificial intelligence laboratory under CPEC at National University of Science and Technology (NUST) has been completed while the equipment installation is almost 100 per cent finished. At the beginning of this year, the laboratory under CPEC--Qingluan Artificial Intelligence Laboratory was officially established at NUST, with joint efforts of NUST and Guangzhou Institute of Chinese Academy of Sciences. Research, development and customisation is currently underway. I would say work is almost finished to 75%." Muhammad Khubaib Shabbir, Deputy Director of China Study Centre of NUST told Gwadar Pro on Saturday. The lab has been put into full use, both students and teaching staff are keen on researching Pattern and Facial Recognition algorithms, the reporter learned. "Currently, Cogniser-V1 intelligent video analysis project-a pilot project with the Government of Pakistan, and a commercial project, namely GymBot are the main projects that are under development." "Ideally, Cognizer-V1 is one of the most sophisticated surveillance equipment, which has the capability of converting ordinary cameras and surveillance equipment into a Smart Equipment, using AI and Computer Vision Algorithms." "To put it simple, the Cognizer-V1 has the ability to sense the people who are lurking around in certain areas and generate warnings, regarding dangerous behavioral patterns such as suicide, or other suspicious activities." In the case of Pakistan, the country is blessed with a large number of artificial intelligence application scenarios and a huge market, thanks to its world's 6th largest population. Moreover, the country is never short on talents. However, challenges lie in the commercialisation of scientific achievements-- an important step which can be viewed as one of the sources for innovation. Due to the backward industrial conditions and obstruction of international exchanges during the epidemic, the progress of commercialization in Pakistani scientific research institutes has been extremely slow. "Our other key project, 'GymBot', can be a perfect example of science commercialization.


Pakistani Gamers Want a Seat at the Table

WIRED

At a Call of Duty tournament in Islamabad, Pakistan, an exasperated gamer stands up from his computer and demands that the player who keeps sniping him speak up. "Who is this '$@dy'?" he bellows, referencing the player's in-game name, his eyes scanning the room in furious anticipation--but what happens next turns his anger into embarrassment, for a diminutive young woman nervously raises her hand. Now, more than 15 years later, Sadia Bashir, 33, recalls the encounter with a glint in her eye. "I was the only girl in a room full of boys, and the moment he saw me, he just sat back down again. I guess the thought of being killed by a girl really hurt his ego." At the time, Bashir was just a computer science major with a dream that she could somehow make a living in the mysterious world of video games.


'DeFi hedge fund' outperforms the market leveraging AI, expands offering

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When asked about wealth inequality, few can say they clearly understand how severe this gap is. For a more precise visual, consider that Elon Musk, reportedly the richest man on Earth, has a network that exceeds the gross domestic products (GDPs) of countries like Portugal, Pakistan and Finland. According to the UN, even a small fraction of his wealth, $6 billion, would save at the very least 42 million people who are on the brink of starvation. Although Musk is just one example, his wealth indicates one thing, a very extreme wealth gap that currently exists, leading many to wonder about the reasons for this. For one, the aphorism "the rich get richer, and the poor get poorer" often comes to mind.


Artificial intelligence in Pakistan - PIAIC Latest News

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Saeed Ghani unveiled big secret - PM Imran Khan and IMF deal? Your feedback is important for us, contact us for any queries.


25,000 students fill National Stadium to take Artificial Intelligence test

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KARACHI: More than 25,000 students from different parts of Sindh participated in the Presidential Initiative for Artificial Intelligence and Computing (PIAIC) grand entrance test 2022 here at the National Stadium Karachi (NSK) on Sunday. The initiative is launched to empower youth through training and providing them financial support to become entrepreneurs. Organised by the Saylani Welfare International Trust (SWIT), the test was witnessed by President Dr Arif Alvi, who also addressed the participants and advised them to work hard and focus on information technology. President Alvi asks youth to avail PTI govt's loans scheme to become entrepreneur "Once you complete your education and training, you would avail thousands of opportunities in this sector across the world," he said in his address before the test. "The government is extending all kinds of support to the IT sector and laws have been made to facilitate the growth of this sector," he said.


Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

arXiv.org Artificial Intelligence

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.


IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN

arXiv.org Artificial Intelligence

Interaction between devices, people, and the Internet has given birth to a new digital communication model, the Internet of Things (IoT). The seamless network of these smart devices is the core of this IoT model. However, on the other hand, integrating smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch an attack to compromise the devices using malware proliferation techniques. Therefore, malware detection is considered a lifeline for the survival of IoT devices against cyberattacks. This study proposes a novel IoT Malware Detection Architecture (iMDA) using squeezing and boosting dilated convolutional neural network (CNN). The proposed architecture exploits the concepts of edge and smoothing, multi-path dilated convolutional operations, channel squeezing, and boosting in CNN. Edge and smoothing operations are employed with split-transform-merge (STM) blocks to extract local structure and minor contrast variation in the malware images. STM blocks performed multi-path dilated convolutional operations, which helped recognize the global structure of malware patterns. Additionally, channel squeezing and merging helped to get the prominent reduced and diverse feature maps, respectively. Channel squeezing and boosting are applied with the help of STM block at the initial, middle and final levels to capture the texture variation along with the depth for the sake of malware pattern hunting. The proposed architecture has shown substantial performance compared with the customized CNN models. The proposed iMDA has achieved Accuracy: 97.93%, F1-Score: 0.9394, Precision: 0.9864, MCC: 0. 8796, Recall: 0.8873, AUC-PR: 0.9689 and AUC-ROC: 0.9938.