Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications, and fiction. To keep track of this data, there are ...

There is increasing attention for audio classification research to support various emerging applications, including environmental monitoring, health care, and smart city. Audio classification is an important area of research ...

In recent years, the Internet of Things (IoT) has received lots of attention due to its promising applications. Along with IoT evolution, we have witnessed advanced research for edge computing and its potential benefits ...

With the increase of density and complexity of high-performance integrated circuits and systems, including many-core chips and system-on-chip (SoC), it is becoming difficult to meet the power delivery and regulation ...

In the photonic technological platforms, the signal is carried by light rather than an electron as in conventional electronic technologies. Electronic processing of the signals is becoming restricted, particularly in the ...

Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. Unfortunately, as software changes, it becomes more prominent and more complicated, which makes ...

Program comprehension is an essential part of software development and maintenance. Traditional methods of program comprehension, such as reviewing the codebase and documentation, are still challenging for understanding ...

Pull requests are widely used in open-source and industrial environments to contribute and assess contributions. Unlike the typical code review process, pull requests provide a more lightweight approach for committing, ...

Existing Federated Learning (FL) relies primarily on client-server architecture to train a single global model utilizing various local datasets. There are ongoing initiatives to improve the present state of federated ...

An essential goal of artificial intelligence is to support the knowledge discovery process from data to the knowledge that is useful in decision making. The challenges in the knowledge discovery process are typically due ...

There have been significant advances in supervised machine learning and enormous benefits from deep learning for a range of diverse applications. Despite the success of deep learning, in reality, very few works have shown ...

Sentiment analysis has proven to be very successful in text applications. Social media is also considered a quite rich source to get data regarding user’s behaviors and preference. Identifying social context would make ...

Understanding users’ context is essential in emerging mobile sensing applications, such as Metal Detector, Glint Finder, Facefirst. Over the last decade, Machine Learning (ML) techniques have evolved dramatically for ...

Deep Learning with depth cameras has enabled 3D hand pose estimation from RGBD images. Commercial solutions like Leap Motion and Intel RealSense™ use stereoscopic sensors or IR illumination-based methods to capture the ...

There are a lot of open source projects available on the internet. Specifically, due to the increasing interest of Deep Learning (DL), the number of DL open source projects is also increased. This project is motivated ...

Twitter and Facebook are the renowned social networking platforms where users post, share, interact and express to the world, their interests, personality, and behavioral information. User-created content on social media ...

In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. ...

Image question answering has gained huge popularity in recent years due to advancements in Deep Learning technologies and computer processing hardware which are able to achieve higher accuracies with faster processing ...

Deep learning has received a lot of attention in the fields such as speech recognition and image classification because of the ability to learn multiple levels of features from raw data. However, 3D deep learning is ...

In recent years, there has been explosive growth in the amount of biomedical data (e.g., publications, notes from EHRs, clinical trial results), with the majority being unstructured data. As the volume of data is ...