Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models have been applied to build non-intrusive speech assessment approaches and confirmed to provide promising performance. However, most DNN-based approaches are designed for normal-hearing listeners without considering hearing-loss factors. In this study, we propose a DNN-based hearing aid speech assessment network (HASA-Net), formed by a bidirectional long short-term memory (BLSTM) model, to predict speech quality and intelligibility scores simultaneously according to input speech signals and specified hearing-loss patterns. To the best of our knowledge, HASA-Net is the first work to incorporate quality and intelligibility assessments utilizing a unified DNN-based non-intrusive model for hearing aids. Experimental results show that the predicted speech quality and intelligibility scores of HASA-Net are highly correlated to two well-known intrusive hearing-aid evaluation metrics, hearing aid speech quality index (HASQI) and hearing aid speech perception index (HASPI), respectively.

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.

Trusted Research environments (TRE)s are safe and secure environments in which researchers can access sensitive data. With the growth and diversity of medical data such as Electronic Health Records (EHR), Medical Imaging and Genomic data, there is an increase in the use of Artificial Intelligence (AI) in general and the subfield of Machine Learning (ML) in particular in the healthcare domain. This generates the desire to disclose new types of outputs from TREs, such as trained machine learning models. Although specific guidelines and policies exists for statistical disclosure controls in TREs, they do not satisfactorily cover these new types of output request. In this paper, we define some of the challenges around the application and disclosure of machine learning for healthcare within TREs. We describe various vulnerabilities the introduction of AI brings to TREs. We also provide an introduction to the different types and levels of risks associated with the disclosure of trained ML models. We finally describe the new research opportunities in developing and adapting policies and tools for safely disclosing machine learning outputs from TREs.

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge'' extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

Summary: Combining neuroimaging and artificial intelligence, researchers identified novel brain signatures unique to the response of each antidepressant. Research led by UT Southwestern has identified MRI brain imaging biomarkers that bring new levels of precision for prescribing the most effective antidepressants. The outcome predictive models were developed in part using data from a large multi-center National Institute of Mental Health-funded study and published in the journal Biological Psychiatry. The findings provide strong evidence that the current trial-and-error approach used in clinical practice for the selection of the right antidepressant can be replaced with this new precision medicine approach. "This is a significant advance. It can be and should be used immediately," said Madhukar Trivedi, M.D., Professor of Clinical Psychiatry, and Director of the Center for Depression Research and Clinical Care, one of the pillars of the Peter O'Donnell Jr. Brain Institute.

The AI industry has expanded massively over the last years and is expected to grow even further. Many of the major corporations employ AI-assisted technicians to solve problems. The median salary of an AI engineer is $150,000 which is up to $171,765 per year. Companies need AI experts who can build and deploy scalable models to meet growing industry demands. There are various AI projects you can do to learn about the library.

Yesterday's edition of The Sequence highlighted OpenAI latest research to solve math word problems. Today, I would like to dive a bit deeper into the ideas behind this new research. Mathematical reasoning has long been considered one of the cornerstones of human cognition and one of the main bars to measure the "intelligence" of language models. He gave 1/2 of his pencils to Brandon, and he gave 3/5 of the remaining pencils to Charlie. He kept the remaining pencils.

Recently many people have been making more and more models using GPT, so we decided to make a model that generates Horoscopes, let AI predict your future! In this article we will describe how we made a horoscope generator and how we trained it. There is only one other model on GitHub that generates horoscopes, and it is a Markov chain, that someone made a long time ago, we took data from here and used it in our own project, more on that later. Markov Chain is a very simple model compared to the GPT models, with GPT we were able to generate more unique horoscopes and much more different ones, than with Markov chain, for this reason, we chose to use GPT instead of Markov chain. We are not world renowned programmers, we were unable to acquire the GPT3 from OpenAI, so we downloaded GPT2 from HuggingFace.

This article was published as a part of the Data Science Blogathon. Keras is a Python library including an API for working with neural networks and deep learning frameworks. Keras includes Python-based methods and components for working with various Deep Learning applications. Tensorflow was previously the most widely used Deep Learning library, however, it was tricky to figure with for newbies. A simple one-layer network involves a substantial amount of code.

Data Science, Machine Learning, Deep Learning and Artificial Learning are all the buzz words from some time right now. We see mobile phones and gadgets with new features coming straight from sci-fi movies and tech companies creating algorithms who knows us better than we do. We see every industry revolutionizing itself with the use of data science including healthcare, finance and entertainment to name a few. And many of us who are not familiar with these terms often find it very difficult to understand what it actually is. Worry no more, here's a simple explanation which can help you comprehend these terms - Artificial Intelligence (AI) is a field of computer science which enables a device to mimic a human intelligence like smart assistance and self-driving cars.