Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning applications with societal impact is fairness (i.e., non-discrimination). Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data. As a countermeasure, fairness testing systemically identifies discriminatory samples, which can be used to retrain the model and improve the model's fairness. Existing fairness testing approaches however have two major limitations. Firstly, they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning models. Secondly, they only work on simple structured (e.g., tabular) data and are not applicable for domains such as text. In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i.e., text classification. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which is significantly more scalable and effective. Experimental results show that on average, our approach explores the search space much more effectively (9.62 and 2.38 times more than the state-of-the-art methods respectively on tabular and text datasets) and generates much more discriminatory samples (24.95 and 2.68 times) within a same reasonable time. Moreover, the retrained models reduce discrimination by 57.2% and 60.2% respectively on average.

A company that makes an implantable brain-computer interface (BCI) has been given the go-ahead by the Food and Drug Administration to run a clinical trial with human patients. Synchron plans to start an early feasibility study of its Stentrode implant later this year at Mount Sinai Hospital, New York with six subjects. The company said it will assess the device's "safety and efficacy in patients with severe paralysis." Before such companies can sell BCIs commercially in the US, they need to prove that the devices work and are safe. The FDA will provide guidance for trials of BCI devices for patients with paralysis or amputation during a webinar on Thursday.

For chief data officers and data scientists, the business case for DataOps can be obvious. DataOps, correctly done, can streamline data workflows, reduce errors, and offers transparency to the entire data operations. It improves efficiency, increases data trust, and gives more time to do analysis. For business executives, such benefits are not immediately apparent. So, getting the budget to build your DataOps can run into snags -- right up until a business problem challenges your company's core value proposition. That's what happened for Screenrights.

The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively-aware AI, but when it will. What will it mean for society when machines -- and the corporations and governments they serve -- can "read" people's minds and emotions? What should developers and operators of such AI do, and what should they not do? The goal of this article is to pre-empt some of the potential implications of these developments, and propose a set of guidelines for evaluating the (moral and) ethical consequences of affectively-aware AI, in order to guide researchers, industry professionals, and policy-makers. We propose a multi-stakeholder analysis framework that separates the ethical responsibilities of AI Developers vis-\`a-vis the entities that deploy such AI -- which we term Operators. Our analysis produces two pillars that clarify the responsibilities of each of these stakeholders: Provable Beneficence, which rests on proving the effectiveness of the AI, and Responsible Stewardship, which governs responsible collection, use, and storage of data and the decisions made from such data. We end with recommendations for researchers, developers, operators, as well as regulators and law-makers.

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x' that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: it allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this paper we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.the choice of pre-trained models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts, but also release other resources, e.g., a website http://pretrain.nlpedia.ai/ including constantly-updated survey, and paperlist.

Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models. Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence. Explicitly, assessing uncertainties associated with DNNs predictions is critical in real-world card fraud detection settings for characteristic reasons, including (a) fraudsters constantly change their strategies, and accordingly, DNNs encounter observations that are not generated by the same process as the training distribution, (b) owing to the time-consuming process, very few transactions are timely checked by professional experts to update DNNs. Therefore, this study proposes three uncertainty quantification (UQ) techniques named Monte Carlo dropout, ensemble, and ensemble Monte Carlo dropout for card fraud detection applied on transaction data. Moreover, to evaluate the predictive uncertainty estimates, UQ confusion matrix and several performance metrics are utilized. Through experimental results, we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions. Additionally, we demonstrate that the proposed UQ methods provide extra insight to the point predictions, leading to elevate the fraud prevention process.

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.

Businesses keep finding new ways to generate value with HPE's real-time global collaboration solution. HPE Pointnext Services can help your organization do the same. Most people are familiar with some type of virtual reality experience from the consumer space, perhaps from trying out some gaming equipment – even if it's borrowed from the kids! If you've ever found yourself immersed in one of those engaging virtual worlds, you'll understand why so many organizations are craving ways to translate augmented reality into the business world. Use cases range from training and education to performing maintenance on facilities and equipment.

Hospitals in Victoria's South West, including public health agencies under the South West Alliance of Rural Health and Barwon Health in Geelong, are set to roll out a data platform capable of real-time analysis using AI, machine learning, as well as business and clinical intelligence. The health organisations will be deploying the IRIS for Health platform by global tech provider InterSystems. The data platform, according to InterSystems's website, is specifically engineered to extract value from healthcare data. It is a standards-based platform that is able to read and write Health Level 7's Fast Healthcare Interoperability Resources (HL7 FHIR) for developing healthcare applications. It is also capable of ingesting, processing and storing transaction data "at high rates" while simultaneously processing high volume analytic workloads involving historical and real-time data. While the health providers have interconnected systems, including clinical and patient administration systems, specialist healthcare applications and data analytics solutions, they don't have a single data repository supporting real-time data analysis.

Volcanologists can predict when volcanos are going to erupt if they have a full detail of its eruptions. But for potentially apocalyptic supervolcanoes, such as the one bubbling under Yellowstone National Park, it's nearly impossible, given how varied their known eruptions have been, according to a new study. Researchers at Cardiff University noted there is not a'single model' that can help scientists understand how eruptions from supervolcanoes happen, making it difficult to understand when they might occur in the future. The researchers looked at geochemical and petrological evidence of 13 supereruptions that have happened over the past 2 million years, including the most recent one, Taupō volcano in New Zealand, which happened more than 24,000 years ago. Experts said there is not a'single model' that can help them understand how eruptions from supervolcanoes happen There was no'single, unified mode' that showed how each of the 13 played out, with some starting gradually over a period of weeks to months, while others exploded suddenly and violently. The researchers also found that the eruptions lasted for varying times, some as short as a period of days or weeks, while others lasted decades.