In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide application area and is a prolific area of research. This paper gives an overview of recent advances in ethnicity classification with focus on convolutional neural networks (CNNs) and proposes a new ethnicity classification method using only the middle part of the face and CNN. The paper also compares the differences in results of CNN with and without plotted landmarks. The proposed model was tested using holdout testing method on UTKFace dataset and FairFace dataset. The accuracy of the model was 80.34% for classification into five classes and 61.74% for classification into seven classes, which is slightly better than state-of-the-art, but it is also important to note that results in this paper are obtained by using only the middle part of the face which reduces the time and resources necessary.

Age estimation is one of the tasks of facial image classification. It can be defined as determination of a person's age or age group from facial images. This paper gives an overview of recent research in facial age estimation. Along with an overview of previous research on this topic, descriptions of basic age estimation models are given: anthropometric model, active appearance model, aging pattern subspace and age manifold.

The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.

Computing in the Internet of Things is increasingly pervasive, with everyday items including clothes, smart-phones, cars and various household appliances gaining sophisticated communication and computing capacities. It seems to be just a matter of time before devices have to collaborate and compete mutually as well as with their users, in order to provide better services to mankind. These embedded computers are increasingly autonomous and connected, and can thus be modeled as agents within multi-agent systems (MAS). Only 30 years ago it was science fiction that over a billion of people will exchange billions of e-mails on a daily basis. Today a scenario of millions of collaborating agents embedded in gadgets and appliances, across various networks may also sound futuristic. However given the current rate of development in electronics, we will soon have to manage large scale MAS (LSMAS) where millions of agents exist, collaborate and compete with each other. While a recent study shows that there are at least 50 organizational structure, superstructure and architecture types employed in modern organizations, there is a lack of research that would apply organizational design methods to organizations composed entirely of agents and agent systems in order to achieve alignment of organizational structure, processes, and reward system with the goals and strategy of the organization. Our research is therefore aimed towards enriching formal design methods for the development of LSMAS to foster the development of self-organizing and adaptable networks of devices that will contribute towards a sustainable development of the information society. In this work-in-progress study we apply a collaborative semantic wiki approach towards formalizing organizational design techniques in order to provide a foundation for future studies of automated LSMAS development

Authors: Miroslav Bača, Petra Grd and Tomislav Fotak

The classification of human face shapes, a pivotal aspect of one’s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model’s robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare.