In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digit...
In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digital nomads (DN). To do this, we collected textual data from posts in a Reddit community. We argue that, in order to understand how to design technical solutions for the so-called ‘new normal’ working conditions, one way to approach this is to understand how digital nomads are being impacted in their work practices and routines, and also how they are seeing the future of their technology-mediated work-life space. Finally, we show how evidence collected from DNs about their experiences and difficulties perceived during the pandemic period can inform CSCW researchers worldwide about future design-oriented strands.
The work proposes the improvement of queue management priority-based Traffic engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calcul...
The work proposes the improvement of queue management priority-based Traffic engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calculations is responsible, firstly, for the distribution and aggregation of packet flows between the macro queues and sub-queues organized on the router interface (congestion management task), and, secondly, for the balanced distribution of interface bandwidth among sub-queues, weighted relative to their priorities (resource allocation task). The upper level of the method's calculations is accountable for distributing the interface's bandwidth between macro queues by performing the iterative procedure. The numerical research results of the proposed two-level queue management method confirmed its effectiveness in ensuring high scalability, balanced priority- based distribution of packet flows and interface bandwidth between the macro queues and sub-queues organized on it. The method demonstrated high coordination procedure convergence and the final quality of centralized calculations.
A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving i...
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Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though ...
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Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier st...
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Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.
computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry ...
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Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as t...
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Human skeleton-based action recognition represents a pivotal field of study, capturing the intricate interplay between physical dynamics and intentional actions. Current research primarily focuses on extracting struct...
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Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This wi...
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ISBN:
(纸本)9798350398830
Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.
Modeling and understanding BitTorrent (BT) dynamics is a recurrent research topic mainly due to its high complexity and tremendous practical efficiency. Over the years, different models have uncovered various phenomen...
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