Development of task guidance systems for aiding humans in a situated task remains a challenging problem. The role of search (information retrieval) and conversational systems for task guidance has immense potential to help the task performers achieve various goals. However, there are several technical challenges that need to be addressed to deliver such conversational systems, where common supervised approaches fail to deliver the expected results in terms of overall performance, user experience and adaptation to realistic conditions. In this preliminary work we first highlight some of the challenges involved during the development of such systems. We then provide an overview of existing datasets available and highlight their limitations. We finally develop a model-in-the-loop wizard-of-oz based data collection tool and perform a pilot experiment.

Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serious privacy concerns. To counter these issues, one of the current state-of-the-art approaches is the Private Aggregation of Teacher Ensembles, or PATE, which achieved promising results in preserving the utility of the model while providing a strong privacy guarantee. PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on. However, the knowledge or voted labels learned by the student are noisy due to private aggregation. Learning directly from noisy labels can significantly impact the accuracy of the student model. In this paper, we propose the PATE++ mechanism, which combines the current advanced noisy label training mechanisms with the original PATE framework to enhance its accuracy. A novel structure of Generative Adversarial Nets (GANs) is developed in order to integrate them effectively. In addition, we develop a novel noisy label detection mechanism for semi-supervised model training to further improve student model performance when training with noisy labels. We evaluate our method on Fashion-MNIST and SVHN to show the improvements on the original PATE on all measures.

Hope is characterized as openness of spirit toward the future, a desire, expectation, and wish for something to happen or to be true that remarkably affects human's state of mind, emotions, behaviors, and decisions. Hope is usually associated with concepts of desired expectations and possibility/probability concerning the future. Despite its importance, hope has rarely been studied as a social media analysis task. This paper presents a hope speech dataset that classifies each tweet first into "Hope" and "Not Hope", then into three fine-grained hope categories: "Generalized Hope", "Realistic Hope", and "Unrealistic Hope" (along with "Not Hope"). English tweets in the first half of 2022 were collected to build this dataset. Furthermore, we describe our annotation process and guidelines in detail and discuss the challenges of classifying hope and the limitations of the existing hope speech detection corpora. In addition, we reported several baselines based on different learning approaches, such as traditional machine learning, deep learning, and transformers, to benchmark our dataset. We evaluated our baselines using weighted-averaged and macro-averaged F1-scores. Observations show that a strict process for annotator selection and detailed annotation guidelines enhanced the dataset's quality. This strict annotation process resulted in promising performance for simple machine learning classifiers with only bi-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset.

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.

Graph learning is a popular approach for performing machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems.

AI researchers at Meta say they have developed the largest protein-folding model of its kind to date, and that it is capable of predicting the structure of more than 600 million proteins.… The team released the 15-billion-parameter ESM-2 transformer-based model and a database of its protein structure predictions, dubbed the ESM Metagenomic Atlas, on Tuesday. This database includes protein shapes that haven't been observed yet by scientists. Proteins are complex biological molecules containing up of 20 types of amino acids, and perform all sorts of biological functions in organisms. Crucially, they fold up into intricate 3D structures, the shape of which is vital to how they operate; knowing their shape helps scientists understand how they function, and from that, helps them figure out ways to mimic, alter, or counter that behavior. Unfortunately, you can't just take the amino acid formula and immediately work out the eventual structure.

Data scientists are still in high demand. They're needed everywhere from startups to Fortune 500 companies, but navigating your career as a data scientist in this job market can be tough because most employers require years of experience. This problem has a simple solution. All you need is a little bit of creativity and a lot of hustle. I've reviewed 1,000's data scientist resumes and talked to dozens of hiring managers.

Visual C programming for desktop application development Published 10/2022 MP4 Video: h264, 1280x720 Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning Language: English Duration: 19 lectures (3h 53m) Size: 1.69 GB Visual C programming for desktop application development What you'll learn Upon successful completion of the course, the students will be able to develop Graphical User Interface (GUI)-based applications using Visual C Students will be able to develop GUI desktop applications in VC for the applications that they have previously made in console environment using C Develop desktop application using VC in the latest version of Microsoft Visual Studio that will enable students to perform various user interface operations Students previously knowing only C will be able to learn how to develop Graphical User Interface applications through VC via easy to learn short tutorials Requirements Basic knowledge of C (console based programming) Basic knowledge of Object-Oriented programming Description Welcome to the course of, Beginning Visual C programming for desktop application development. This is a must to take course if you have just learned the basic C using console interface and wondering how various user-interface applications can be created using C . This course will enable you to understand the basics of desktop application development using the latest version of Microsoft's visual studio. The teaching methodology of this course is based on hands-on topic specific examples that enable quicker learning. In this course, you will be learning VC using the latest version of Microsoft's visual studio.

In this course, you will learn all the techniques used by real time Data Scientists and the various methods involved in Machine learning using Python. GreyCampus is an online training platform, that has been in this field for more than 12 years. Before we set up a curriculum, we ensure that the learner understands the core fundamentals of any course they opt for. That's why our curriculum begins at the basics and ends at how to deal advance level topics. Python is a powerful tool used to analyse data, create beautiful visualisations, and use powerful machine learning algorithms!

Massive geospatial data are generated every second from our smartphones, through our social media posts, or through many kinds of other means like tracked whale trajectories in the ocean, allowing us to trace the movements of entire societies. As these data keep growing, it becomes more important to extract meaningful insights from location, relation, and position, for applications as diverse as business analytics, epidemiology, or species protection. This course provides students core competences in Geospatial Data Science (GDS). A prerequisite for taking this course is solid know-how in Python programming and data analysis. There are 14 weeks of learning/teaching activities.