Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitation of the current model. Overall, as our analysis shows, Event Linking is a considerably challenging and essential task requiring more effort from the community. Data and code are available here: https://github.com/CogComp/event-linking.

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets. In this work, we develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs, in which we jointly encode a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. Furthermore, our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments. We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks. We also conduct in-depth analysis in terms of the improvement and the limitation of pairwise representation so as to provide insights for future work.

In place of in-house solutions, organizations are increasingly moving towards managed services for cyber defense. Security Operations Centers are specialized cybersecurity units responsible for the defense of an organization, but the large-scale centralization of threat detection is causing SOCs to endure an overwhelming amount of false positive alerts -- a phenomenon known as alert fatigue. Large collections of imprecise sensors, an inability to adapt to known false positives, evolution of the threat landscape, and inefficient use of analyst time all contribute to the alert fatigue problem. To combat these issues, we present That Escalated Quickly (TEQ), a machine learning framework that reduces alert fatigue with minimal changes to SOC workflows by predicting alert-level and incident-level actionability. On real-world data, the system is able to reduce the time it takes to respond to actionable incidents by $22.9\%$, suppress $54\%$ of false positives with a $95.1\%$ detection rate, and reduce the number of alerts an analyst needs to investigate within singular incidents by $14\%$.

The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study reveals the machine's decision-making logic, allowing us to learn from the machine to feedback to the traditional analysis. In this work, we have presented the first machine learning analysis of the data from the Majorana Demonstrator; this is also the first interpretable machine learning analysis of any germanium detector experiment. Two gradient boosted decision tree models are trained to learn from the data, and a game-theory-based model interpretability study is conducted to understand the origin of the classification power. By learning from data, this analysis recognizes the correlations among reconstruction parameters to further enhance the background rejection performance. By learning from the machine, this analysis reveals the importance of new background categories to reciprocally benefit the standard Majorana analysis. This model is highly compatible with next-generation germanium detector experiments like LEGEND since it can be simultaneously trained on a large number of detectors.

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.

Miscommunication and communication challenges between instructors and students represents one of the primary barriers to post-secondary learning. Students often avoid or miss opportunities to ask questions during office hours due to insecurities or scheduling conflicts. Moreover, students need to work at their own pace to have the freedom and time for the self-contemplation needed to build conceptual understanding and develop creative thinking skills. To eliminate barriers to student engagement, academic institutions need to redefine their fundamental approach to education by proposing flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a power language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system will serve as a voice-enabled helper capable of answering course-specific questions concerning curriculum, logistics and course policies. It is envisioned to improve access to course-related information for the students and reduce logistical workload for the instructors and TAs. Its GPT-3-based knowledge discovery component as well as the generalized system architecture is presented accompanied by a methodical evaluation of the system accuracy and performance.

American defense officials on Tuesday sought to dispel any doubt that Iran is supplying drones for Russia's war in Ukraine, releasing photos and analysis of unmanned aircraft deployed in the conflict to demonstrate Tehran's involvement. During a briefing in London, analysts from the Defense Intelligence Agency displayed photos of drones that attacked Ukraine alongside images of those previously traced to Iran. A comparison of design details such as tail fins, nose cones and landing gear shows that the weapons used in Ukraine are "indistinguishable" from Shahed-131 and -136 attack drones and Mohajer 6 unmanned aerial vehicles used in the Middle East. The effort to "show the homework'' is intended to help persuade governments or international agencies of Tehran's involvement. Iran has said it supplied a "small number" of drones to Russia before the invasion of Ukraine but has denied providing any more since troops crossed the border last February. The evidence proves otherwise, an official from the Defense Intelligence Agency said while speaking on condition of anonymity because of the sensitivity of the information. "Iran is a partner in the conflict with Russia,'' the official said.

Starting today, Meta is rolling out a new version of its "Why am I seeing this ad?" tool. The company says the redesigned interface is meant to provide users with more information about how their activities on Facebook and beyond inform the machine learning models that power its ad-matching software. If you're unfamiliar with the tool, you can access it by clicking or tapping the three dots icon next to an ad on Facebook or Instagram. Once you have access to the updated tool, you'll see a summary of the actions on Meta's platforms and other websites that may have informed the company's machine-learning models. For instance, the page may note that you're seeing an ad for a dress or suit because you interacted with style content on Facebook.

ChatGPT was predicted to have 100 million monthly active users two months after its introduction. The rapidly expanding artificial intelligence program ChatGPT has been met with acclaim for its speed and versatility in providing written responses to various questions and worries from US politicians about the technology's potential effects on national security and education. Having reached 100 million monthly active users within two months after its release, ChatGPT was the fastest-growing consumer app in history. The private business OpenAI, sponsored by Microsoft Corp., has released it at no cost to the public. Since generative AIs like ChatGPT are pervasive, teachers and students fear that they may be abused to distribute false information and facilitate cheating.

The risk landscape of AI is broad and evolving. For instance, ML models, which are often developed using vast, complex, and continuously updated datasets, require a high level of digitization and connectivity in software and engineering pipelines. Yet the eradication of IT silos, both within the enterprise and potentially with external partners, increases the attack surface for cyber criminals and hackers. Cyber security and resilience is an essential component of the digital transformation agenda on which AI depends. A second established risk is bias. Because historical social inequities are baked into raw data, they can be codified--and magnified--in automated decisions leading, for instance, to unfair credit, loan, and insurance decisions.