Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models.

Article states that vaccines, though reliable preventative measures for diseases, also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines including the COVID-19 vaccines. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis.

Article that introduces Natlog, a lightweight Logic Programming language, sharing Prolog's unification-driven execution model, but with a simplified syntax and semantics. The authors' proof-of-concept Natlog implementation is tightly embedded in the Python-based deep-learning ecosystem with focus on content-driven indexing of ground term datasets. As an overriding of the authors symbolic indexing algorithm, the same function can be delegated to a neural network, serving ground facts to Natlog's resolution engine. The open-source implementation is available as a Python package at t https://pypi.org/project/natlog/.

Article on deep-learning framework for edge intelligence EDL-EI (event-driven deep learning for edge intelligence). To verify the proposed framework, the authors include a case study of air-quality scenarios based for the most polluted cities in South Korea and China.

This article uses a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses provide insights into the effects of global mass gatherings on the progression of the COVID-19 pandemic locally and globally.

This article aims to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. It was presented at the 36th International Conference on Logic Programming (ICLP).

Article proposes a two-stage training dense point cloud generation network.

This article discusses a novel risk equation for estimating new infections among Females who Sell Sex (FWSS), their clients, and communities.

This article introduces a novel multi-objective attack response system.

This paper presents an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications.

This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.

This paper describes a co-training approach that uses the text and citation information of a research article as two different views to predict the topic of an article.

This article discusses dynamic intimate contact social networks and epidemic interventions.

Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

This presentation discusses the research of sensor synchronization, sensor grid routing, and voice over internet protocol (VoIP).

This paper discusses a time synchronization scheme for wireless sensor networks that aims to conserve sensor battery power while maintaining network connectivity for as long as possible.

This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier.

This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.

This paper discusses parity assisted decision making for QAM modulation.

This paper discusses reconfigurable block encryption logic. Existing block cipher function designs have tended to deploy the secret bits in a specific and limited way. The authors generalize the role of the secret as truth tables of Boolean gates in a carefully designed logic schema.

In this paper, the authors design and implement a local session admission control (SAC) algorithm for third-generation wireless networks which allows for the simulation of network throughput for different spreading factors and various mobility scenarios.

Article discussing parallel texts and natural language processing.

This paper discusses non-preemptive, real-time scheduling.

In this paper, the authors present a knowledge-based method for measuring the semantic-similarity of texts. Through experiments performed on two different applications: (1) paraphrase and entailment identification, and (2) word sense similarity, the authors show that this method outperforms the traditional text similarity metrics based on lexical matching.