Source code authorship attribution is an important problem often encountered in applications such as software forensics, bug fixing, and software quality analysis. Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation. This calls for robust solutions to the problem of code authorship attribution. In this paper, we initiate the study on making Deep Learning (DL)-based code authorship attribution robust. We propose an innovative framework called Robust coding style Patterns Generation (RoPGen), which essentially learns authors' unique coding style patterns that are hard for attackers to manipulate or imitate. The key idea is to combine data augmentation and gradient augmentation at the adversarial training phase. This effectively increases the diversity of training examples, generates meaningful perturbations to gradients of deep neural networks, and learns diversified representations of coding styles. We evaluate the effectiveness of RoPGen using four datasets of programs written in C, C++, and Java. Experimental results show that RoPGen can significantly improve the robustness of DL-based code authorship attribution, by respectively reducing 22.8% and 41.0% of the success rate of targeted and untargeted attacks on average.

The first phase of developing an intelligent system is the selection of an ontology of symbols representing relevant concepts of the application domain. These symbols are then used to represent the knowledge of the domain. This representation should be \emph{elaboration tolerant}, in the sense that it should be convenient to modify it to take into account new knowledge or requirements. Unfortunately, current formalisms require a significant rewrite of that representation when the new knowledge is about the \emph{concepts} themselves: the developer needs to "\emph{reify}" them. This happens, for example, when the new knowledge is about the number of concepts that satisfy some conditions. The value of expressing knowledge about concepts, or "intensions", has been well-established in \emph{modal logic}. However, the formalism of modal logic cannot represent the quantifications and aggregates over concepts that some applications need. To address this problem, we developed an extension of first order logic that allows referring to the \emph{intension} of a symbol, i.e., to the concept it represents. We implemented this extension in IDP-Z3, a reasoning engine for FO($\cdot$) (aka FO-dot), a logic-based knowledge representation language. This extension makes the formalism more elaboration tolerant, but also introduces the possibility of syntactically incorrect formula. Hence, we developed a guarding mechanism to make formula syntactically correct, and a method to verify correctness. The complexity of this method is linear with the length of the formula. This paper describes these extensions, how their relate to intensions in modal logic and other formalisms, and how they allowed representing the knowledge of four different problem domains in an elaboration tolerant way.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential. This can potentially include anything from customer lists to algorithms.

ANJA KASPERSEN: Today I am joined by Joel Rosenthal and Stelios Vassilakis for an irreverently engaging conversation about the impact of artificial intelligence (AI) on democracy, what we can learn from the Athenian agora in preserving what it means to be human in the biodigital realm, and how ethics empower civil engagement. Stelios Vassilakis is co-directing programs and strategic initiatives at the Stavros Niarchos Foundation, which is one of the leading international philanthropic organizations. Stelios is also a classics and modern Greek studies scholar, specializing in the works of Homer. Joel Rosenthal is president of Carnegie Council for Ethics in International Affairs and a distinguished public intellectual of international relations and foreign policy. Before handing the floor over to Joel to guide us through this conversation, I am very curious about these concepts that are guiding the work of both of your institutions. For the Stavros Niarchos Foundation it is empowering humanity, and for Carnegie Council it is about empowering ethics, and obviously there is a strong link between the two. I think in today's world we live in a very distrustful world, a crowded and overheated public space--if we can even identify that space, which we have talked about is a difficult space to even find--and so what we are trying to do at the beginning to empower ethics is first of all just to identify the issues, and to identify these issues, put a name on them, label them, and show them to be issues of competing values and competing interests that would benefit from reflection, dialogue, and discussion, even that question of identification and clarification of these issues and to bring them to the fore in a way that will not necessarily lead to polarization but can lead to constructive dialogue. The second step is to provide thought leadership around these questions--there are people who have dedicated their lives to thinking about some of these issues and to studying these issues; they have great competence and some authority in speaking about these issues--and to identify those people and bring that thought leadership to bear on these questions. Critically, though, it is not just about thinking. It is also about experience. There are people who are actually working on these issues, they are working these problems. It is part of their personal and professional life, and I think that the experience that they have themselves is almost as valuable if not more valuable than those who spend their lives thinking about these issues and creating scholarship around them. So when we talk about thought leadership we're talking about both scholarship and lived experience, Carnegie Council being a place where we can bring that expertise, if you will, to bear on these questions. The third part that is also critical today is to create a community of engagement around these issues.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential.

Existing work (Ji and Grishman, we consider the attention weight between 2008; McClosky et al., 2011; Li et al., 2013; two event mentions as a learned similarity, and we Chen et al., 2015; Du and Cardie, 2020; Li et al., ensure that the attention mechanism learns to align 2021a) traditionally uses a predefined list of event similar events using a semi-supervised contrastive types and their respective annotations to learn an loss. By doing this, we are able to leverage the event extraction model. However, these annotations large variety of semantic information in pretrained are both expensive and time-consuming to language models for clustering unseen types by using create. This problem is amplified when considering a trained attention head. Unlike (Huang and specialization-intensive domains such as scientific Ji, 2020), we are able to separate clustering from literature, which requires years of specialized experience learning, allowing specific task-suited clustering to understand even a specific niche. For algorithms to be selected.

A US federal court has sentenced Canadian hacker Doug Bowser to 40 months in prison for his involvement in Switch hacking group Team Xecuter, the Department of Justice announced on Thursday. Not to be confused with Nintendo of America president Doug Bowser (or Mario's nemesis, for that matter), Bowser was part of a collective that developed and sold devices people could use to play pirated games on their consoles. The FBI arrested Bowser in 2020. One year later, he agreed to pay $10 million to Nintendo to settle a civil privacy lawsuit and another $4.5 million in restitution to the company. Leading up to today's sentencing announcement, Bowser faced up to 10 years in prison.

Back in 2015, Google was called out for its photo app that mistakenly labeled pictures of people with darker skin as gorillas. As you can imagine, it was a PR disaster. Of course, the company publicly apologized, said that such a result is unacceptable, and promised to fix the mistake. But apparently – as Wired uncovered three and a half years later – Google somehow never got to fixing the underlying issue. Instead, it implemented a workaround, blocking its AI from identifying gorillas (and other primates) altogether to prevent another miscategorization.

This is a rush transcript of "Tucker Carlson Tonight" on February 9, 2022. This copy may not be in its final form and may be updated. It would be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly out of nowhere started calling in unison for medical freedom. Suddenly, they sound like Bobby Kennedy, Jr., pretty much all of them, even the whiny hypochondriacs at "The Atlantic" Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all corona restrictions. Open everything, "The time to end pandemic restrictions is now." Believe it or not, that was the headline on "The Atlantic's" website today. So if even "The Atlantic" has given up on corona restrictions, obviously the pandemic is over. You should know this virus was killed not by science, but by the midterm elections. It turns out the only real cure for COVID-19 is the political ambition of the Democratic Party. Yes, every upside has a downside. It means that pasty NPR listeners are going to emerge from their apartment for the first time in two years, they will be loose on the streets. You're going to see them at Whole Foods again, shuffling along with their tote bags, looking bewildered and annoyed. That's bad, but it's still worth it, anything to make the insanity go away, we're celebrating. But we're also looking forward, and the question is, how do we guarantee that nothing like this ever happens again? How do we prevent future mass hysteria events in the United States?

Robotics4EU is a 3-years-long EU-funded project which advocates for a wider adoption for AI-based robots in 4 sectors: healthcare, inspection and maintenance of infrastructure, agri-food, and agile production. Thus, Robotics4EU raises awareness about non-technological aspects in robotics through delivering a series of workshops to involve the research community, industry representatives and citizens. Social impact of Robotics in Inspection and Maintenance" which took place on the 26th of January, 2022 tackled the problem of interactions between robots and humans. How to evaluate the real impact of robotics on our society? How to decide if robots are hazardous and may complicate human lives? To what extent does society accept rapidly progressing robotic technologies?