Chinese philosophy yin and yang represent how the seemingly opposite poles can complement each other and achieve harmony. In cybersecurity, this ancient philosophy perfectly represents the relationship between supervised and unsupervised machine learning. For example, monitored machine learning processes can be used for detection, while unsupervised machine learning uses clustering. In the case of cybersecurity and data security research and development, monitored machine learning is often implemented in the form of machine learning algorithms. It is not easy to describe Artificial Intelligence (AI). It has no clear definition.

As we see, Indian Government is totally focused to improve Indian technology and trying to make self reliance initiative more successful so there are a lot of announcement happening everyday. Recently, Tamil Nadu Chief Minister Edapaddi K Palaniswami has unveiled safe & Ethical Artificial Intelligence Policy 2020 during CII Connect 2020. The agenda is to provide a framework for inclusive, safe and ethical use of Artificial Intelligence (AI) in government domain. It also aims to build fairness, equity, transparency and trust in AI assisted decision making systems. The plan of Artificial Intelligence Policy 2020 is to build a mature and self-sustaining AI community to aid the growth of AI in the State and to train and skill people in AI, the policy document said.

By Abhay Pendse We are living in a digital age where digital ecosystems form the backbone of our day to day lives. Cyberattacks are increasingly targeting the digital ecosystems. As advances in Artificial Intelligence (AI) and Machine learning (ML) move at breakneck speed, use of AI and ML is expected to grow at heartening pace in cyber defences and will add tremendous intelligence and power to fight against cyberattacks. Cybersecurity attacks keep growing at an alarming speed and keep getting more sophisticated with IoT attacks, data breaches, spam and phishing, crypto jacking, mobile malware and ransomware. Data losses and disruption due to these attacks continue to be significant for businesses and organizations, both in monetary terms and in damage to their reputations.

Most national AI strategies and major ethical guidelines make only cursory mention of children and their specific needs. For country policies, references to children are usually about preparing them as a future AI workforce. But as children increasingly use or are affected by AI systems in everyday situations -- from playing with robotic toys that listen, observe and talk, to interacting with voice assistants -- the lack of attention on the opportunities and risks that AI systems hold for children is growing. To help fill this gap, the Office of Global Insight and Policy is leading a two-year project to explore approaches to protecting and upholding child rights in an evolving AI world. We are supported by and partnering with the Government of Finland, and collaborating with the IEEE Standards Association, the Berkman Klein Centre for Internet & Society, the World Economic Forum, the 5Rights Foundation and other organizations that form part of Generation AI.

We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.

COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.

In manufacturing, unexpected failures are considered a primary operational risk, as they can hinder productivity and can incur huge losses. State-of-the-art Prognostics and Health Management (PHM) systems incorporate Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain the health status of equipment, and thus reduce the downtime, maintenance cost and increase the productivity. Unfortunately, IoT sensors and DL algorithms, both are vulnerable to cyber attacks, and hence pose a significant threat to PHM systems. In this paper, we adopt the adversarial example crafting techniques from the computer vision domain and apply them to the PHM domain. Specifically, we craft adversarial examples using the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and apply them on the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) based PHM models. We evaluate the impact of adversarial attacks using NASA's turbofan engine dataset. The obtained results show that all the evaluated PHM models are vulnerable to adversarial attacks and can cause a serious defect in the remaining useful life estimation. The obtained results also show that the crafted adversarial examples are highly transferable and may cause significant damages to PHM systems.

The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop.

In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.

Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving. However, only a few works have explored GNNs' robustness to adversarial attacks, and their designs are usually limited by the scale of input datasets (i.e., focusing on small graphs with only thousands of nodes). In this work, we propose, SAG, the first scalable adversarial attack method with Alternating Direction Method of Multipliers (ADMM). We first decouple the large-scale graph into several smaller graph partitions and cast the original problem into several subproblems. Then, we propose to solve these subproblems using projected gradient descent on both the graph topology and the node features that lead to considerably lower memory consumption compared to the conventional attack methods. Rigorous experiments further demonstrate that SAG can significantly reduce the computation and memory overhead compared with the state-of-the-art approach, making SAG applicable towards graphs with large size of nodes and edges.