Mobile health apps provide a unique means of collecting data that can be used to deliver adaptive interventions.The predicted outcomes considerably influence the selection of such interventions. Recourse via counterfactuals provides tangible mechanisms to modify user predictions. By identifying plausible actions that increase the likelihood of a desired prediction, stakeholders are afforded agency over their predictions. Furthermore, recourse mechanisms enable counterfactual reasoning that can help provide insights into candidates for causal interventional features. We demonstrate the feasibility of GAN-generated recourse for mobile health applications on ensemble-survival-analysis-based prediction of medium-term engagement in the Safe Delivery App, a digital training tool for skilled birth attendants.

Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.

Precipitation governs Earth's hydroclimate, and its daily spatiotemporal fluctuations have major socioeconomic effects. Advances in Numerical weather prediction (NWP) have been measured by the improvement of forecasts for various physical fields such as temperature and pressure; however, large biases exist in precipitation prediction. We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times. To hybridise, we address the sphericity of the global data by using modified DLWP-CS architecture which transforms all the fields to cubed-sphere projection. Dynamical model precipitation and surface temperature outputs are fed into a modified DLWP-CS (UNET) to forecast ground truth precipitation. While CFSv2's average bias is +5 to +7 mm/day over land, the multivariate deep learning model decreases it to within -1 to +1 mm/day. Hurricane Katrina in 2005, Hurricane Ivan in 2004, China floods in 2010, India floods in 2005, and Myanmar storm Nargis in 2008 are used to confirm the substantial enhancement in the skill for the hybrid dynamical-deep learning model. CFSv2 typically shows a moderate to large bias in the spatial pattern and overestimates the precipitation at short-range time scales. The proposed deep learning augmented NWP model can address these biases and vastly improve the spatial pattern and magnitude of predicted precipitation. Deep learning enhanced CFSv2 reduces mean bias by 8x over important land regions for 1 day lead compared to CFSv2. The spatio-temporal deep learning system opens pathways to further the precision and accuracy in global short-range precipitation forecasts.

Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindled, most work has focused on memory recall ($read$) over memory learning ($write$). In this paper, we present BayesPCN, a hierarchical associative memory capable of performing continual one-shot memory writes without meta-learning. Moreover, BayesPCN is able to gradually forget past observations ($forget$) to free its memory. Experiments show that BayesPCN can recall corrupted i.i.d. high-dimensional data observed hundreds to a thousand ``timesteps'' ago without a large drop in recall ability compared to the state-of-the-art offline-learned parametric memory models.

Hazardous Lighting Market size was valued at $1.8 billion in 2020 and it is estimated to grow at a CAGR of 2.29% during 2021-2026. The growth is mainly attributed to the increasing investment on various industries, high penetration of internet of things (IoT), increasing demand for efficient advanced lighting solutions across industries and rapid industrialization in emerging economies. Furthermore, the constant innovation in advanced technologies such as artificial intelligence (AI), machine learning (ML), radio-frequency identification (RFID) along with other wireless technologies, which are being used for producing advanced connected hazardous lighting system; and awareness regarding energy conservation boost the growth of hazardous lighting market. Furthermore, government's initiatives for greener strategies to support sustainable development across the world, is one of the major driving factors of hazardous lighting industry. Hence, the above mentioned factors will drive the adoption rate of various hazardous lighting solutions such as industrial LED lighting, fluorescent lighting, high-intensity discharge lamps and others, during the forecast period 2021-2026.

This week, Microsoft President and vice chair Brad Smith is heading to Egypt for the United Nation's annual climate conference with a mission: show the world that the tech giant is "consistent and committed" in its climate goals, as well as communicate the "vital role" that the tech industry as a whole has to play in battling the climate crisis. The Microsoft leader has been busy in recent months since the departure of chief environmental officer Lucas Joppa, stepping in to lead the company's climate initiatives (something Smith has always been intimately involved with, as Joppa's boss prior to his departure). Last week at the Web Summit tech conference, he spoke about the urgency of the workforce transformation the world needs to reach net zero, as well as the current skills gap. "The key to the future is going to be a new generation of people with a new generation of technology coming from a new generation of companies," he said, highlighting the work of startups like the India-based SEEDS, which is using satellite data and AI to identify homes that would be most susceptible to extreme heat, then helping them adapt. Using AI and data to help the Global South adapt to climate change is one of Microsoft's main focuses going into the COP27 climate talks.

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.

Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning.

This paper describes a French dialect recognition system that will appropriately distinguish between different regional French dialects. A corpus of five regions - Monaco, French-speaking, Belgium, French-speaking Switzerland, French-speaking Canada and France, which is targeted forconstruction by the Sketch Engine. The content of the corpus is related to the four themes of eating, drinking, sleeping and living, which are closely linked to popular life. The experimental results were obtained through the processing of a python coded pre-processor and Waikato Environment for Knowledge Analysis (WEKA) data analytic tool which contains many filters and classifiers for machine learning.

Image captioning models tend to describe images in an object-centric way, emphasising visible objects. But image descriptions can also abstract away from objects and describe the type of scene depicted. In this paper, we explore the potential of a state-of-the-art Vision and Language model, VinVL, to caption images at the scene level using (1) a novel dataset which pairs images with both object-centric and scene descriptions. Through (2) an in-depth analysis of the effect of the fine-tuning, we show (3) that a small amount of curated data suffices to generate scene descriptions without losing the capability to identify object-level concepts in the scene; the model acquires a more holistic view of the image compared to when object-centric descriptions are generated. We discuss the parallels between these results and insights from computational and cognitive science research on scene perception.