As an open and collaborative knowledge graph created by users and bots, it is possible that the knowledge in Wikidata is biased in regards to multiple factors such as gender, race, and country of citizenship. Previous work has mostly studied the representativeness of Wikidata knowledge in terms of genders of people. In this paper, we examine the race and citizenship bias in general and in regards to STEM representation for scientists, software developers, and engineers. By comparing Wikidata queries to real-world datasets, we identify the differences in representation to characterize the biases present in Wikidata. Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented. Based on these findings, we have found and linked to Wikidata additional data about STEM scientists from the minorities. This data is ready to be inserted into Wikidata with a bot. Increasing representation of minority race and country of citizenship groups can create a more accurate portrayal of individuals in STEM.

Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-scale datasets. We summarize three issues leading to such high time-space complexity in existing EA methods: (1) Inefficient graph encoders, (2) Dilemma of negative sampling, and (3) "Catastrophic forgetting" in semi-supervised learning. To address these challenges, we propose a novel EA method with three new components to enable high Performance, high Scalability, and high Robustness (PSR): (1) Simplified graph encoder with relational graph sampling, (2) Symmetric negative-free alignment loss, and (3) Incremental semi-supervised learning. Furthermore, we conduct detailed experiments on several public datasets to examine the effectiveness and efficiency of our proposed method. The experimental results show that PSR not only surpasses the previous SOTA in performance but also has impressive scalability and robustness.

Human Activity Recognition (HAR) based on IMU sensors is a crucial area in ubiquitous computing. Because of the trend of deploying AI on IoT devices or smartphones, more researchers are designing different HAR models for embedded devices. Deployment of models in embedded devices can help enhance the efficiency of HAR. We propose a multi-level HAR modeling pipeline called Stage-Logits-Memory Distillation (SMLDist) for constructing deep convolutional HAR models with embedded hardware support. SMLDist includes stage distillation, memory distillation, and logits distillation. Stage distillation constrains the learning direction of the intermediate features. The teacher model teaches the student models how to explain and store the inner relationship among high-dimensional features based on Hopfield networks in memory distillation. Logits distillation builds logits distilled by a smoothed conditional rule to preserve the probability distribution and enhance the softer target accuracy. We compare the accuracy, F1 macro score, and energy cost on embedded platforms of a MobileNet V3 model built by SMLDist with various state-of-the-art HAR frameworks. The product model has a good balance with robustness and efficiency. SMLDist can also compress models with a minor performance loss at an equal compression ratio to other advanced knowledge distillation methods on seven public datasets.

In the digital world, top AI companies in social good are coming up with innovative AI solutions to address worldly challenges. Artificial intelligence for social good is making significant impacts in diverse industries.

Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. Berkeley-based project could support action worldwide on climate, health, and poverty. More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information -- including data that reflects climate change, health, and poverty -- to databases on the ground. There's just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.

Tree squirrels are the Olympic divers of the rodent world, leaping gracefully among branches and structures high above the ground. And as with human divers, a squirrel's success in this competition requires both physical strength and mental adaptability. Two species – the eastern gray squirrel (Sciurus carolinensis) and the fox squirrel (Sciurus niger) – thrive on campus landscapes and are willing participants in our behavioral experiments. They are also masters in two- and three-dimensional spatial orientation – using sensory cues to move through space. In a newly published study, we show that squirrels leap and land without falling by making trade-offs between the distance they have to cover and the springiness of their takeoff perch.

A federal court in Australia has ruled that AI systems can be credited as inventors under patent law in a case that could set a global precedent. Ryan Abbott, a professor at University of Surrey, has launched over a dozen patent applications around the world – including in the UK, US, New Zealand, and Australia – on behalf of US-based Dr Stephen Thaler. The twist here is that it's not Thaler which Abbott is attempting to credit as an inventor, but rather his AI device known as DABUS. "In my view, an inventor as recognised under the act can be an artificial intelligence system or device," said justice Jonathan Beach, overturning Australia's original verdict. "We are both created and create. Why cannot our own creations also create?"

Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise. This model mimics the nature of scientific data sets, where the noises arise as realizations of some random processes whose underlying distributions are known. The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.

But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption. Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting.