Forecasts help businesses allocate resources and achieve objectives. At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. Engineers use forecasts to efficiently provision hardware. Developing a forecasting solution to meet these needs requires accurate and interpretable forecasts on diverse time series with sub-hourly to quarterly frequencies. We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn. Its flagship algorithm, Silverkite, provides interpretable, fast, and highly flexible univariate forecasts that capture effects such as time-varying growth and seasonality, autocorrelation, holidays, and regressors. The library enables self-serve accuracy and trust by facilitating data exploration, model configuration, execution, and interpretation. Our benchmark results show excellent out-of-the-box speed and accuracy on datasets from a variety of domains. Over the past two years, Greykite forecasts have been trusted by Finance, Engineering, and Product teams for resource planning and allocation, target setting and progress tracking, anomaly detection and root cause analysis. We expect Greykite to be useful to forecast practitioners with similar applications who need accurate, interpretable forecasts that capture complex dynamics common to time series related to human activity.

We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (AE) which is interpreted probabilistically after training using a Normalizing Flow (NF). We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population, and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multi-stage training setup alongside our physically-parameterized network we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an RMS of $0.091 \pm 0.010$ mag, which corresponds to $0.074 \pm 0.010$ mag if peculiar velocity contributions are removed. Trained models and codes are released at \href{https://github.com/georgestein/suPAErnova}{github.com/georgestein/suPAErnova}

The wide use of social media and digital technologies facilitates sharing various news and information about events and activities. Despite sharing positive information misleading and false information is also spreading on social media. There have been efforts in identifying such misleading information both manually by human experts and automatic tools. Manual effort does not scale well due to the high volume of information, containing factual claims, are appearing online. Therefore, automatically identifying check-worthy claims can be very useful for human experts. In this study, we describe our participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing steps and applied different models to identify whether a given text is worthy of fact checking or not. We use the oversampling technique to balance the dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models for the experiments. We used BERT-m for the official submissions and our systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English, respectively. In further experiments, our evaluation shows that transformer models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and English languages where a different scenario is observed for Spanish.

Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex black boxes. This means that a security analyst will have little to no explanation or clarification on why an IDS model made a particular prediction. A potential solution to this problem is to research and develop Explainable Intrusion Detection Systems (X-IDS) based on current capabilities in Explainable Artificial Intelligence (XAI). In this paper, we create a Self Organizing Maps (SOMs) based X-IDS system that is capable of producing explanatory visualizations. We leverage SOM's explainability to create both global and local explanations. An analyst can use global explanations to get a general idea of how a particular IDS model computes predictions. Local explanations are generated for individual datapoints to explain why a certain prediction value was computed. Furthermore, our SOM based X-IDS was evaluated on both explanation generation and traditional accuracy tests using the NSL-KDD and the CIC-IDS-2017 datasets.

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.

BOSTON, July 14, 2022 (GLOBE NEWSWIRE) -- juli, the AI-powered digital health platform that delights and engages consumers to power their own health while offering their healthcare providers insights from sub-episodic health data, announced the formation of an advisory board packed with digital health luminaries. Launched more than a year ago, juli helps people manage complex chronic conditions by aggregating and analyzing data from EMRs, smartphones, wearables, the environment, and patient-reported data. By applying AI to these disparate data sources, juli identifies previously unseen correlations and encourages micro-behavioral changes in users that can help alleviate conditions such as depression, bipolar disorder, asthma, migraine, and chronic pain. Joe Kvedar, MD - Professor at Harvard Medical School and digital health pioneer, Kvedar is also Editor in Chief of Nature's npj Digital Medicine and just completed his term as Chair of the Board for the American Telemedicine Association. Kristen Valdes - Founder and CEO of b.well Connected Health, the digital transformation platform, and a former UnitedHealthcare executive, Valdes has over 20 years' experience making healthcare data more easily interoperable with less friction for health plan members and consumers, including as a board member of the CARIN Alliance.

CHILLING AI satellite swarms that hunt down and destroy enemy targets have been unveiled by China in another terrifying step in the space race. Chinese scientists said they could now launch hundreds of mini satellites - dubbed "cubesats" - from a large motherboard in space with deadly precision and speed. Weighing in at 2.2lbs, these tiny satellites are so complex they can only be controlled by Artificial Intelligence (AI). According to researchers, the complexity of a large scale space battle would be so immense that it's beyond the human brain and even beyond some powerful algorithms, the South China Morning Post reports. The study, published in the peer-reviewed journal Chinese Space Science and Technology, said unlocking the right AI to control the motherboard and cubesats would have "strong economic and military value".

Soon after Roe v. Wade was overturned, a neonatal nurse took to a local Ohio newspaper to share how strongly she agreed with the Supreme Court's opinion. Instead of explicitly expressing religious views or personal beliefs, she shared that in her "professional experience" the 1973 cementing of national abortion rights "led to the utter demise of respect for humanity at any lifestage and has, singlehandedly, led to a demise in our societal culture and ethical values." She noted that 99 percent of people seeking abortions are doing so "as a birth control method." The newspaper piece is a startling artifact of the anti-choice movement. The American College of Obstetricians and Gynecologists is firm in its own stance: "Abortion is an essential component of comprehensive, evidence-based health care."

The Centers for Disease Control and Prevention is leveraging machine learning to predict the spread of COVID-19. The Postal Service is using the technology to speed package delivery. The Transportation Department is piloting use of ML to forecast structural safety of highway bridges. The Defense Department is testing it to visualize terrain and "see" around obstacles. In fact, a growing number of federal agencies are exploring ML and other forms of artificial intelligence to further...