The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is an opportunity to also improve the explainability of AI by leveraging best practices of social epidemiology and health equity to help us develop hypotheses for associations found. In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review to discuss and critically assess AI model explanations from multiple perspectives and identify areas of bias and directions for future research. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations which are historically and contextually informed. Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature. In turn, these insights can suggest opportunities for AI model improvement.

Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multi-headed attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (re-calibration-free). Our evaluations across diverse indoor environments show 8-75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2 years of temporal variations, showcasing its robustness and adaptability.

CyberGuy explains why you should never give someone your phone number on dating apps. Online dating can sometimes lead to love, and it can sometimes lead to talking to a lot of weirdos on the internet. However, a weirdo is definitely better than a scammer. We received an email from one of our CyberGuy Report Newsletter subscribers who said they were having a typical conversation on Tinder before they were asked to share their number and move the conversation to WhatsApp. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER "Kurt, Some dating red flags: Scammers will say their husband was killed in a car accident, now they are living with their Aunt. Yesterday on Tinder I asked, "what do you do for a living?". The scripted questions that followed included, "My Aunt and I own a jewelry shop!", "what are you looking for on here?", "How long have you been on this dating site?" [The scammer] then lured me so to WhatsApp we could talk more. This happened 15 mins into the chat. Gave me her phone number and asked for mine. Very clever - easy to get pulled in".

Law makers ask President Obama for more water pumping from Sacramento Delta.

Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.

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The cornerstone of collaboration is based on knowledge transfer; sharing of research tools, methodologies and findings; and sometimes combining mutual funding resources to meet shortfalls necessary to build prototypes and commercialize technologies. Collaborations often involve combinations of government, industry and academia who work together to meet difficult challenges and cultivate new ideas. A growing trend for many leading companies is creating technology specific innovation centers, labs, and foundries to accelerate collaboration and invention. As the development of new technologies continues to grow exponentially and globally, collaboration has more value as a resource for adapting to the rapidly emerging technologies landscape by establishing pivotal connections between companies, technologies and stakeholders. In the US Federal government, the National Labs (including: Lawrence Livermore, Oak Ridge, Argonne, Sandia, Idaho National Laboratory, Battelle, and Brookhaven, and Federally Funded Research and Development Centers (FFRDC's), and federally funded Centers For Excellence have been outlets for innovation and public/private cooperation.

Drone shows are quickly becoming the tool of choice for people and companies that want to grab your attention, and Genesis knows that all too well. The Hyundai-owned car brand marked its entrance into China by breaking the Guinness World Record for the most Unmanned Aerial Vehicles in the air at the same time, using 3,281 drones to display its logo and otherwise advertise over Shanghai on March 29th. The company flew'just' 3,051 drones in September 2020. That, in turn, smashed a record set by a 2,200-drone performance in Russia just days earlier. Intel, which has a reputation for drone light shows, last claimed the record with 2,066 drones flying over Folsom, California in July 2018.

Comprehensive specifications are essential for various activities across the entire validation continuum for system-on-chip (SoC) designs. However, specifications are often ambiguous, incomplete, or even contain inconsistencies or errors. This paper addresses this problem by developing a specification mining approach that automatically extracts sequential patterns from SoC transaction-level traces such that the mined patterns collectively characterize system-level specifications for SoC designs. This approach exploits long short-term memory (LSTM) networks trained with the collected SoC execution traces to capture sequential dependencies among various communication events. Then, a novel algorithm is developed to efficiently extract sequential patterns on system-level communications from the trained LSTM models. Several trace processing techniques are also proposed to enhance the mining performance. We evaluate the proposed approach on simulation traces of a non-trivial multi-core SoC prototype. Initial results show that the proposed approach is capable of extracting various patterns on system-level specifications from the highly concurrent SoC execution traces.

It is built with having both the developer and non-programmers in mind. Ginibot offers functionalities I have not seen with any other chatbots, like built-in ecommerce system, perfect for small to medium size online stores, empowered by an intelligent CRM that allows companies to present and sell their products on any sales channels, like Facebook Messenger, Slack, Skype, and even instagram in a conversational way. Ginibot supports 184 languages and is equipped with NLP and NLU capabilities.