This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.

As algorithmic risk assessment instruments (RAIs) are increasingly adopted to assist decision makers, their predictive performance and potential to promote inequity have come under scrutiny. However, while most studies examine these tools in isolation, researchers have come to recognize that assessing their impact requires understanding the behavior of their human interactants. In this paper, building off of several recent crowdsourcing works focused on criminal justice, we conduct a vignette study in which laypersons are tasked with predicting future re-arrests. Our key findings are as follows: (1) Participants often predict that an offender will be rearrested even when they deem the likelihood of re-arrest to be well below 50%; (2) Participants do not anchor on the RAI's predictions; (3) The time spent on the survey varies widely across participants and most cases are assessed in less than 10 seconds; (4) Judicial decisions, unlike participants' predictions, depend in part on factors that are orthogonal to the likelihood of re-arrest. These results highlight the influence of several crucial but often overlooked design decisions and concerns around generalizability when constructing crowdsourcing studies to analyze the impacts of RAIs.

The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks. We observe that since there are similar backgrounds in target domains, it can apply self-labeled samples as prior tasks to transfer knowledge onto target tasks. To this end, we propose a task-expansion-decomposition framework for CD-FSL, called Self-Taught (ST) approach, which alleviates the problem of non-target guidance by constructing task-oriented metric spaces. Specifically, Weakly Supervised Object Localization (WSOL) and self-supervised technologies are employed to enrich task-oriented samples by exchanging and rotating the discriminative regions, which generates a more abundant task set. Then these tasks are decomposed into several tasks to finish the task of few-shot recognition and rotation classification. It helps to transfer the source knowledge onto the target tasks and focus on discriminative regions. We conduct extensive experiments under the cross-domain setting including 8 target domains: CUB, Cars, Places, Plantae, CropDieases, EuroSAT, ISIC, and ChestX. Experimental results demonstrate that the proposed ST approach is applicable to various metric-based models, and provides promising improvements in CD-FSL.

The University of Florida recently turned on the eighth most powerful supercomputer in higher education and 22nd most powerful supercomputer in the world. And added 100 new AI-focused faculty to the already several hundred who are engaged in AI. It's a complete transformation of higher education, built on artificial intelligence as a core competency. Joseph Glover, Provost and Senior VP of Academic Affairs, told me recently on the TechFirst podcast. "The College of Business just made AI a required introductory course for their entering freshmen ... we believe that this is going to be a transformational initiative for the University of Florida. We think that this is where higher education is going to inevitably go."

In a recent white paper, former Scholastic president of education Margery Mayer dubbed 2021 the "year of speech recognition" in education. And she may be right: A spike in adoption by edtech developers in the first half of this year reflects the recognition that technology holds the potential to not only create more engaging learning experiences for students, but to transform the very practice of early literacy instruction altogether. In prior years, such a vision may have seemed far fetched. But as EdSurge has previously noted, the science behind speech recognition for children has begun to come of age, enabling educational applications that have piqued the interest of edtech developers, educators and researchers alike. Part of what has enabled the growing use of speech recognition in education is the availability today of technology built specifically to cater to kids' voices and behaviors.

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Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain. Most adaptation methods rely on the source data by joint optimization over source data and target data. Source-free methods replace the source data with a source model by fine-tuning it on target. Either way, the majority of the parameter updates for the model representation and the classifier are derived from the source, and not the target. However, target accuracy is the goal, and so we argue for optimizing as much as possible on the target data. We show significant improvement by on-target adaptation, which learns the representation purely from target data while taking only the source predictions for supervision. In the long-tailed classification setting, we show further improvement by on-target class distribution learning, which learns the (im)balance of classes from target data.

Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, possible coexistence of multiple learning tasks with different datasets may arise. The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation. To this end, we aim to develop an energy-efficient framework for learners-orchestrator association and learning task allocation, in which each orchestrator gets associated with a group of learners with the same learning task based on their communication channel qualities and computational resources, and allocate the tasks accordingly. Therein, a multi objective optimization problem is formulated to minimize the total energy consumption and maximize the learning tasks' accuracy. However, solving such optimization problem requires centralization and the presence of the whole environment information at a single entity, which becomes impractical in large-scale systems. To reduce the solution complexity and to enable solution decentralization, we propose lightweight heuristic algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity. Simulation results show that the proposed approaches reduce the energy consumption significantly while executing multiple learning tasks compared to recent state-of-the-art methods.

If Anna Matsumoto had listened to her teachers, she would have kept her inquisitive mind to herself -- asking questions, they told her, interrupted class. And when, at age 15, she had to choose a course of study in her Japanese high school, she would have avoided science, a track that her male teachers said was difficult for girls. Instead, Matsumoto plans to become an engineer. Japan could use a lot more young women like her. Despite its tech-savvy image and economic heft, the country is a digital laggard, with a traditional paperbound office culture where fax machines and personal seals known as hanko remain common.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Rage Against the Machine's Tom Morello is asking for the public's help in assisting female guitar students out of Afghanistan following the Taliban takeover of the capital city, Kabul. Morello penned an open letter calling attention to his friend and musician Lanny Cordola's "Girl with a Guitar" program, which currently has 12 female guitar students ranging in age from 8 to 17 years old who are stuck in Kabul. The girls are students of a music school in Afghanistan Cordola helped bring to fruition through a non-profit called Miraculous Love Kids.