Articulated motion and deformable objects

This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field

Intro to This Special Issue: Refugees/Displaced People in the Workplace

This special issue focuses on refugees’ experiences and displaced people across a diverse set of ethnicities and circumstances. The growing number of refugees and displaced people and the work and life difficulties they face are central social issues in the world today. This special issue will explore how refugees and displaced people in Brazil can be fully integrated, socialized, engaged, embraced, and affirmed into the workplace and society. Research is presented on the experiences of refugees and displaced people, a growing but under-researched segment of the world’s population. Little is known about refugees’ career experiences and displaced people and how organizations, leaders, and policymakers can assist them in finding work, maintaining employment, and creating positive life outcomes. There are 12 articles included in this special issue. They focus on three areas of refugees in the workplace. The first area explores biases in the perceptions of refugees based on factors such as skin complexion, countries of origin, and race. The second area presents research that elaborates on the theme of displacement of refugees and barriers to integration, inclusion, social recognition, and belonging. The third area examines ways in which refugees have been integrated and acculturated into Brazilian society, often through the assistance of NGOs or through the efforts of managers in the workplace. It is our hope that the research presented in this special issue will increase interest in this important topic and lead to additional future research related to reducing barriers to integration and acculturation that refugees and displaced people face

Machine learning paradigms for modeling spatial and temporal information in multimedia data mining

Multimedia data mining and knowledge discovery is a fast emerging interdisciplinary applied research area. There is tremendous potential for effective use of multimedia data mining (MDM) through intelligent analysis. Diverse application areas are increasingly relying on multimedia under-standing systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, machine learning, pattern recognition, multimedia databases, and smart sensors. The main mission of this special issue is to identify state-of-the-art machine learning paradigms that are particularly powerful and effective for modeling and combining temporal and spatial media cues such as audio, visual, and face information and for accomplishing tasks of multimedia data mining and knowledge discovery. These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics. A number of papers have been submitted to the special issue in the areas of imaging, artificial intelligence; and pattern recognition and five contributions have been selected covering state-of-the-art algorithms and advanced related topics. The first contribution by D. Xiang et al. “Evaluation of data quality and drought monitoring capability of FY-3A MERSI data” describes some basic parameters and major technical indicators of the FY-3A, and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. The second contribution by A. Belatreche et al. “Computing with biologically inspired neural oscillators: application to color image segmentation” investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to gray scale and color image segmentation, an important task in image understanding and object recognition. The major contribution of this paper is the ability to use neural oscillators as a learning scheme for solving real world engineering problems. The third paper by A. Dargazany et al. entitled “Multibandwidth Kernel-based object tracking” explores new methods for object tracking using the mean shift (MS). A bandwidth-handling MS technique is deployed in which the tracker reach the global mode of the density function not requiring a specific staring point. It has been proven via experiments that the Gradual Multibandwidth Mean Shift tracking algorithm can converge faster than the conventional kernel-based object tracking (known as the mean shift). The fourth contribution by S. Alzu’bi et al. entitled “3D medical volume segmentation using hybrid multi-resolution statistical approaches” studies new 3D volume segmentation using multiresolution statistical approaches based on discrete wavelet transform and hidden Markov models. This system commonly reduced the percentage error achieved using the traditional 2D segmentation techniques by several percent. Furthermore, a contribution by G. Cabanes et al. entitled “Unsupervised topographic learning for spatiotemporal data mining” proposes a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency Identification (RFID) data. The new unsupervised algorithm depicted in this article is an efficient data mining tool for behavioral studies based on RFID technology. It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning. Finally, we would like to thank all those who helped to make this special issue possible, especially the authors and the reviewers of the articles. Our thanks go to the Hindawi staff and personnel, the journal Manager in bringing about the issue and giving us the opportunity to edit this special issue

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Editorial

While our journal enjoys increasing recognition by international readers and authors, it also remains a vital forum for Swiss communication and media studies. The current issue testifies to this attachment, as it includes research from universities in Switzerland’s German-, French-, and Italian-speaking parts. The General Section opens with two articles addressing the micro level of in-person communication and the macro level of mass-mediated discourses in the public sphere. In the first article, Jonathan Gruber, Eszter Hargittai, and Minh Hao Nguyen from the University of Zurich investigate the value of face-to-face communication in a world where digital communication technologies are omnipresent. The researchers draw on survey data collected in the U.S. when the first COVID-19-related lockdown limited in-person interactions. They use this opportunity to study what people value in face-to-face interactions, as this likely becomes more salient to people when in-person interactions are less available. Their results show that most people missed elements of face-to-face interaction, such as the special value of spontaneous conversation and physical closeness. The study also sheds light on which modes of digital communication seem to compensate for the lack of face-to-face interactions better than others

Editorial

While our journal enjoys increasing recognition by international readers and authors, it also remains a vital forum for Swiss communication and media studies. The current issue testifies to this attachment, as it includes research from universities in Switzerland’s German-, French-, and Italian-speaking parts. The General Section opens with two articles addressing the micro level of in-person communication and the macro level of mass-mediated discourses in the public sphere. In the first article, Jonathan Gruber, Eszter Hargittai, and Minh Hao Nguyen from the University of Zurich investigate the value of face-to-face communication in a world where digital communication technologies are omnipresent. The researchers draw on survey data collected in the U.S. when the first COVID-19-related lockdown limited in-person interactions. They use this opportunity to study what people value in face-to-face interactions, as this likely becomes more salient to people when in-person interactions are less available. Their results show that most people missed elements of face-to-face interaction, such as the special value of spontaneous conversation and physical closeness. The study also sheds light on which modes of digital communication seem to compensate for the lack of face-to-face interactions better than others