Physical fitness presents a significant challenge in ensuring proper exercise posture. Individuals who work out need help maintaining correct exercise posture during their workouts. Maintaining correct form is critical for ensuring the safety and effectiveness of fitness routines. Yet, it is often challenging for individuals to keep proper form without professional guidance, which usually comes at expensive costs. The paper presents a novel method that utilizes the capabilities of YOLOv7 and a primary web camera to offer immediate feedback and correction on body posture during gym activities. Such a method empowers individuals to correct themselves and promotes motivation even without the presence of a professional trainer. This system has been developed to provide immediate, personalized feedback for various fitness exercises. It efficiently counts repetitions and provides textual guidance for improvement, tailored to the specific requirements of fitness enthusiasts. To determine the efficacy of our technology, we carried out a user study in a controlled laboratory setting simulating a gym environment. The study compares our interactive system with the traditional training method, involving participants of varied fitness levels. It showed significant improvements in exercise technique with real-time feedback. These findings are crucial for AI-supported training systems in strength training, underscoring the need for adaptive technologies for different user experiences. The research contributes to human-computer interaction and fitness technology discussions, highlighting interactive models’ potential to augment and sometimes replicate personal training benefits in exercise form and posture improvement.

Summary Online social networks such as Facebook and Twitter have become part of our daily lives. Their influence on business, politics, and society is considerable. Sensitive or unreliable information can adversely affect individuals, organizations, and governments. Due to the effects of the Covid‐19 epidemic, online news is more plentiful and accessible, which raises concerns about its reliability, quality, and authenticity. This article proposes the use of population dynamics model to study information dissemination on Facebook and a Susceptible‐Infected‐Recovered (SIR) model to examine information propagation as an outbreak of disease. We investigated 27 datasets with more than 270,000 messages, and the experiments showed that the population dynamics model is suitable for modeling the spread of information. The results revealed that information propagation could occur rapidly; after only 1–2 days. Additionally, we discovered that it is very crucial to find immediate solutions for preventing fake information as soon as it appears. This work enables us to understand the mechanism of information dissemination on social networks. This can help control and prevent the spread of misleading information, avoiding unintended consequences.

The cash on delivery (CoD) is currently one of the significant payment mechanism in many developing countries' E-commerce systems. A transaction between a seller and a purchaser is completed when the seller has agreed to exchange packaged goods for a payment from the purchaser. Building highly trustworthy, accountable, credible, and decentralized CoD systems to trace and track physical items is a very challenging task. Several technologies and models for deploying CoD-based applications have been proposed in the literature. In most scenarios, these models have been created with the aim of accommodating collaborative processes involving multiple participants, e.g. seller, purchasers, and shippers, that belong to independent organizations. However, these approaches face several limitations and require appropriate improvement to sustain CoD-based systems' adoption further. First of all, there are no incentives for participants to act honestly. Secondly, the shippers are often kept out of the delivery chain and are affected by or not involved in any incentives or logs. Last but not least, users' privacy can be easily compromised and sensitive data collected and generated during the transactions can be easily accessed and abused. Building upon these critical insights, we propose a novel decentralized marketplace mechanism using the smart contract via blockchain technology. Our approach operates by incentivizing all the participants to act honestly and fulfill their obligations without resorting to a trusted third party. It also integrates adapted access control protocols to protect user privacy. The model's architecture shows that our approach guarantees integrity and robustness. It contributes to effectively addressing the issues listed above. A complete code solution is publicized on the authors' GitHub repository to engage further reproducibility and improvement.

Topic modeling is a widely used technique in knowledge discovery and data mining. However, finding the right number of topics in a given text source has remained a challenging issue. In this paper, we study the concept of conceptual stability via nonnegative matrix factorization. Based on this finding, we propose a method to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the text sources. Experiments on real-world text corpora demonstrate that the proposed method has outperformed state-of-the-art latent Dirichlet allocation and nonnegative matrix factorization models.

Previous research on content-based geolocation in general has developed prediction methods via conducting pre-partitioning and applying classification methods. The input of these methods is the concatenation of individual tweets during a period of time. But unfortunately, these methods have some drawbacks. They discard the natural real-values properties of latitude and longitude as well as fail to capture geolocation in near real-time. In this work, we develop a novel generative content-based regression model via a matrix factorization technique to tackle the near real-time geolocation prediction problem. With this model, we aim to address a couple of un-answered questions. First, we prove that near real-time geolocation prediction can be accomplished if we leave out the concatenation. Second, we account the real-values properties of physical coordinates within a regression solution. We apply our model on Twitter datasets as an example to prove the effectiveness and generality. Our experimental results show that the proposed model, in the best scenario, outperforms a set of state-of-the-art regression models including Support Vector Machines and Factorization Machines by a reduction of the median localization error up to 79%.