We propose a method to predict toxicity and other textual attributes through the use of natural language processing (NLP) techniques for two recent events: the Ukraine-Russia and Hamas-Israel conflicts. This article provides a basis for exploration in future conflicts with hopes to mitigate risk through the analysis of social media before and after a conflict begins. Our work compiles several datasets from Twitter and Reddit for both conflicts in a before and after separation with an aim of predicting a future state of social media for avoidance. More specifically, we show that: (1) there is a noticeable difference in social media discussion leading up to and following a conflict and (2) social media discourse on platforms like Twitter and Reddit is useful in identifying future conflicts before they arise. Our results show that through the use of advanced NLP techniques (both supervised and unsupervised) toxicity and other attributes about language before and after a conflict is predictable with a low error of nearly 1.2 percent for both conflicts.

IL is one of the mechanisms expounded by Complementary Learning Systems Theory (McClelland, McNaughton and O'Reilly, 1995; Marr, 1971) on how successful learners such as human beings mitigate effects of'catastrophic interference' while learning. Recent illustrations of IL using neural networks include Saxena, Shobe and McNaughton, 2022, who exhibited that if the new information is similar to a subset of old items, then deep neural networks can learn the new information rapidly and with the same level of accuracy by interleaving the old items in the subset. A similar insight was presented in McClelland, McNaughton and Lampinen, 2020, where it was shown that for artificial neural networks, information consistent with prior knowledge can sometimes be integrated very quickly. Another recent paper (Ban and Xie, 2021) formulated interleaved machine learning as a multi-level optimization problem, and developed an efficient differentiable algorithm to solve the interleaving learning problem with application to neural architecture search. A closely related biological concept is interleaved replay which also has been empirically validated in the literature (Gepperth and Karaoguz, 2016; Kemker and Kanan, 2018). Over the past couple of decades, ideas inspired by biological IL have been utilized in a wide array of online learning methods as well, especially to prevent catastrophic forgetting. See, for example Wang et.

Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner. However, the non-stationary nature of streaming training data poses a significant challenge known as catastrophic forgetting, which refers to the rapid forgetting of previously learned knowledge when new tasks are introduced. While some approaches, such as experience replay (ER), have been proposed to mitigate this issue, their performance remains limited, particularly in the class-incremental scenario which is considered natural and highly challenging. In this paper, we present a novel algorithm, called adaptive-experience replay (AdaER), to address the challenge of continual lifelong learning. AdaER consists of two stages: memory replay and memory update. In the memory replay stage, AdaER introduces a contextually-cued memory recall (C-CMR) strategy, which selectively replays memories that are most conflicting with the current input data in terms of both data and task. Additionally, AdaER incorporates an entropy-balanced reservoir sampling (E-BRS) strategy to enhance the performance of the memory buffer by maximizing information entropy. To evaluate the effectiveness of AdaER, we conduct experiments on established supervised continual lifelong learning benchmarks, specifically focusing on class-incremental learning scenarios. The results demonstrate that AdaER outperforms existing continual lifelong learning baselines, highlighting its efficacy in mitigating catastrophic forgetting and improving learning performance.

Computing competency is becoming an essential quality needed by industry. For decades, the gap between baccalaureate computing graduates and industry needs was a discussion topic. Most graduates seek employment in deference to continuing their full-time graduate (master's or doctoral) programs. While the percent of such choice varies by institution, it is estimated that about 5% of computing graduates choose full-time graduate study upon graduation, meaning that 95% of computing graduates seek jobs in business, government, or industry.15 While computing graduates may acquire jobs in today's world, they often lack the competencies (skills and dispositions) expected in the workplace. Most undergraduate computing-degree programs want to produce job-ready graduates who are productive on the first workday. They often seek local advisory boards composed of industry, government, and business representatives to help develop a functional computing curriculum for their students. Information technology and computing disciplines are changing, and new fields appear continuously. Computing curricula and undergraduate programs are challenged to keep up with this rapid change. Employers are looking for competent graduates who can apply the knowledge, skill, and culture they acquire in college to solve problems as soon as they enter the workforce. High-performance computing (HPC) and parallel and distributed computing (PDC) have become pervasive.

Nonconvex penalties are utilized for regularization in high-dimensional statistical learning algorithms primarily because they yield unbiased or nearly unbiased estimators for the parameters in the model. Nonconvex penalties existing in the literature such as SCAD, MCP, Laplace and arctan have a singularity at origin which makes them useful also for variable selection. However, in several high-dimensional frameworks such as deep learning, variable selection is less of a concern. In this paper, we present a nonconvex penalty which is smooth at origin. The paper includes asymptotic results for ordinary least squares estimators regularized with the new penalty function, showing asymptotic bias that vanishes exponentially fast. We also conducted an empirical study employing deep neural network architecture on three datasets and convolutional neural network on four datasets. The empirical study showed better performance for the new regularization approach in five out of the seven datasets.

The contentious first presidential debate between Hillary Clinton and Donald Trump lived up to its big ratings expectations with an estimated average TV viewership that will top the previous record of 80.6 million. The total average audience for Monday's matchup for the ad-supported broadcast and cable networks as well as PBS came in at about 84 million, according to Nielsen numbers. Monday's faceoff tops the previous record for a presidential debate set when Jimmy Carter and Ronald Reagan clashed on Oct. 28, 1980. It was their only meeting of that year's presidential campaign, which occurred in an era when U.S. households had only a handful of channels to choose from. The total across broadcast and cable networks measured by Nielsen for the Clinton-Trump debate does not include viewers who watched the debate through various video streams available online.

The first presidential debate between Hillary Clinton and Donald Trump is in the books. I tweeted, took notes and picked some winners and losers. At times she came across as overly rehearsed and robotic. This week, the advertising world converges on New York City to discuss the industry and its ongoing changes. The team at Advertising Week has created a program full of interesting speakers and topics, including brand storytelling, mobile advertising and diversity.