In this paper, we study the problem of recovering the community structure of a network under federated myopic learning. Under this paradigm, we have several clients, each of them having a myopic view, i.e., observing a small subgraph of the network. Each client sends a censored evidence graph to a central server. We provide an efficient algorithm, which computes a consensus signed weighted graph from clients evidence, and recovers the underlying network structure in the central server. We analyze the topological structure conditions of the network, as well as the signal and noise levels of the clients that allow for recovery of the network structure. Our analysis shows that exact recovery is possible and can be achieved in polynomial time. We also provide information-theoretic limits for the central server to recover the network structure from any single client evidence. Finally, as a byproduct of our analysis, we provide a novel Cheeger-type inequality for general signed weighted graphs.

At a 2020 meeting of the World Economic Forum in Davos, Salesforce founder Marc Benioff declared that "capitalism as we have known it is dead." In its place now is stakeholder capitalism, a form of capitalism that has been spearheaded by Klaus Schwab, founder of the World Economic Forum, over the past 50 years. As Benioff put it, stakeholder capitalism is "a more fair, a more just, a more equitable, a more sustainable way of doing business that values all stakeholders, as well as all shareholders." Unlike shareholder capitalism, which is measured primarily by the monetary profit generated for a business' shareholders alone, stakeholder capitalism requires that business activity should benefit all stakeholders associated with the business. These stakeholders can include the shareholders, the employees, the customers, the local community, the environment, etc.

We devise a novel conditional tabular data synthesizer, CTAB-GAN, that addresses the limitations of the prior state-of-the-art: (i) encoding mixed data type of continuous and categorical variables, (ii) efficient modeling of long tail continuous variables and (iii) increased robustness to imbalanced categorical variables along with skewed continuous variables. Furthermore, two key features of CTAB-GAN are the introduction of classification loss in conditional GAN, and novel encoding for the conditional vector that efficiently encodes mixed variables and helps to deal with highly skewed distributions for continuous variables.

Litigating Artificial Intelligence: When Does AI Violate Our Legal Rights? Read full article May 27, 2021, 3:20 PM ·3 min read From the minds of Canada's leading law and technology experts comes a playbook for understanding the multi-faceted intersection of AI and the law TORONTO, May 27, 2021 (GLOBE NEWSWIRE) -- We are living in an Artificial Intelligence (AI) boom. Self-driving cars, personal voice assistants, and facial recognition technology are just a few of the AI-enabled technologies permeating into everyday life. But what happens when AI causes harm or violates our rights? If your self-driving car gets into an accident while on autopilot, are you responsible? Emond Publishing, Canada's leading independent legal publisher, today announced the release of Litigating Artificial Intelligence, a book examining AI-informed legal determinations, AI-based lawsuits, and AI-enabled litigation tools. Anchored by the expertise of general editors Jill R. Presser, Jesse Beatson, and Gerald Chan, this title offers practical insights regarding AI's decision-making capabilities, position in evidence law and product-based lawsuits, role in automating legal work, and use by the courts, tribunals, and government agencies. For example, can government agencies use AI-powered facial recognition software to identify BLM protestors and Capitol rioters, or does this violate privacy rights? Who is liable, users, developers, or AI? What laws are in place to prevent AI-related crimes, and how do litigators prosecute the responsible parties?

Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups or political campaigns) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches (e.g., how to detect adversarial (image) examples), and there are associated real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.

In March 2020, two months after The New York Times exposed that Clearview AI had scraped billions of images from the internet to create a facial recognition database, Thomas Smith received a dossier encompassing most of his digital life. Using the recently enacted California Consumer Privacy Act, Smith asked Clearview for what they had on him. The company sent him pictures that spanned moments throughout his adult life: a photo from when he got married and started a blog with his wife, another when he was profiled by his college's alumni magazine, even a profile photo from a Python coding meetup he had attended a few years ago. "That's what really threw me: All the things that I had posted to Facebook and figured, 'Nobody's going to ever look for that,' and here it is all laid out in a database," Smith told The Verge. Clearview's massive surveillance apparatus claims to hold 3 billion photos, accessible to any law enforcement agency with a subscription, and it's likely you or people you know have been scooped up in the company's dragnet.

The conundrum is one that many governments face: how do you make the most of technological advances in areas such as artificial intelligence (AI) while protecting people's rights? This applies to government as both a user of the tech and a regulator with a mandate to protect the public. Australia's Human Rights Commission recently undertook an exercise to consider this very question. Its final report, Human Rights and Technology, was published recently and includes some 38 recommendations – from establishing an AI Safety Commissioner to introducing legislation so that a person is notified when a company uses AI in a decision that affects them. We have rounded up some of the report's recommendations for governments about how to ensure greater use of AI-informed decision-making does not result in human rights disaster.

We propose a large, scalable engineering knowledge graph, comprising sets of (entity, relationship, entity) triples that are real-world engineering facts found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain the engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Nebraska man who murdered and dismembered a young woman he met on Tinder and then slashed his own neck in court during his trial was sentenced to death this week. Aubrey Trail, a 54-year-old thief and con man, was convicted of strangling 24-year-old Sydney Loofe with an electrical cord in 2017, then cutting her body into 14 pieces that he dumped in various rural roadside ditches. At trial, witnesses testified that Trail and his girlfriend, Bailey Boswell, 27, had solicited them for group sex and talked of the occult and gaining "powers" through killing.

Artificial intelligence (A.I.), one of the 20 core technologies I identified back in 1983 as the drivers of exponential economic value creation, has started out simple. From Amazon's Alexa, Siri on your iPhone, or proclaiming "hey, Google…" in your home, there are several small but impactful applications of A.I. that have become fully integrated in our world today. Now, following a historic moment in contemporary history dominated by a global pandemic, A.I. advancements have been turbocharged like never before. Consumer products that implement A.I. that have been in the spotlight for a handful of years are now having to share that fame with Information Technology (IT) solutions and its place in industry. If you haven't already, from this point forward, it would be a good idea to keep a closer eye on A.I.'s rapid development and look for both predictable problems as well as amazing opportunities.