Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.

The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essential tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.

… operations and a culture of innovation, resulting in GDIT’s acceleration of enhanced offerings in cloud, mobility and artificial intelligence to DOD.

Artificial intelligence (AI) is disrupting technologies, markets and applications everywhere. But what does it really mean? Professors Andreas Kaplan and Michael Haenlein defined AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation." AI's ascendancy has been made possible by exponential advances in computing power and the proliferation of devices capturing quantum of data. During this pandemic, we have a new appreciation for the international exchange of information about the spread of COVID-19, and the artificial intelligence technologies that are powering models about the spread and how to control it are critical to human survival.

Digital transformation continues to multiply the potential attack surface exponentially, bringing new opportunities for the cyber-criminal community. In addition to their expanding arsenal of sophisticated malware and zero day threats, AI and machine learning are new tools being added to their toolbox. To the surprise of almost no-one, AI is being weaponized by cyber adversaries. Leveraging AI and automation enables bad actors to commit more attacks at a faster rate – and that means security teams are going to have to likewise quicken their speed to keep up. Adding fuel to the fire, this is happening in real-time, and we're seeing rapid development, so there is little time for deciding whether to deploy your own AI countermeasures.

The Israeli army says one of its drones came down in Lebanese territory, following a reinforcement of its presence at its northern frontier near Lebanon. The drone fell "during IDF operational activity" along the border, the army said in a statement on Sunday. "There is no concern that any information was leaked," it said. Israel's Channel 12 reported that the drone crashed after it experienced a technical failure. Tensions have risen along Israel's frontier with Syria and Lebanon this week after a fighter from the Iranian-backed Lebanese group Hezbollah was killed in an apparent Israeli strike on the edge of Damascus.

Usually, you pop up in an exhibition, coming from vivid streets to the Silent Hall of art. The exhibition pops up where you are, suddenly, amidst your next Zoom call, or while you are checking your emails. And you are exposed to it. In these crazy pandemic times, they found a perfect way to present art, without put the visitors in danger to be Corona'ed: Plug-In. You install a plug-in to your browser, and every hour another artwork overfloods your PC windows.

BHUBANESWAR: In its bid to ensure greater fiscal accountability, effective resources management and increased productivity, the state government is set to go for integration of artificial intelligence (AI) in the financial auditing mechanism for its organisations in a big way. The Directorate of Local Fund Audit (DLFA) has started work on an AI project that is aimed at reshaping organisational accountability and bringing about greater trust on Government institutions. It is already seeking advisory services from experts for the successful implementation of the technology infusion in local fund audit automation. The Government has appointed Professor (Information Systems) of XIMB Sanjay Mohapatra as Advisor to the AI implementation committee of DLFA. He will assist LFA to develop a comprehensive strategy to analyze processes and develop implementation plans for AI-enabled automation and advise on available options and capabilities along with skilling of officials As per the project, the audit of organisations will be conducted using various components of AI like machine learning, deep learning, natural language processing and computer vision.

It helps drive your car, recognizes your face at the airport's immigration checkpoint, interprets your CT scans, reads …

Although the COVID-19 pandemic has impacted all areas of the globe especially international travel, supply chains and business models everywhere, 2020 started as 2019 ended, with new cyberattacks, hacking incidents and data breaches coming to light almost every day. The digital age has been one of technology increase at an exponential rate. In the recorded history of man, every age has been one of slow progress, linear at best but slowly over the years, centuries even millennia. The introduction of the personal computer (PC) in the 1980s and microchip technology with Moore's famous observational law of technology doubling the number of transistors in a microchip every two years at about half the cost is mindboggling. This has held true for over 40 years.