Effects of whole body vibration training on body composition, skeletal muscle strength, and cardiovascular health

Whole body vibration training (WBVT) has been used as a supplement to conventional exercise training such as resistance exercise training to improve skeletal muscle strength, specifically, in rehabilitation field. Recently, this exercise modality has been utilized by cardiovascular studies to examine whether WBVT can be a useful exercise modality to improve cardiovascular health. These studies reported that WBVT has not only beneficial effects on muscular strength but also cardiovascular health in elderly and disease population. However, its mechanism underlying the beneficial effects of WBVT in cardiovascular health has not been well documented. Therefore, this review highlighted the impacts of WBVT on cardiovascular health, and its mechanisms in conjunction with the improved muscular strength and body composition in various populations

Investigating Job Mismatch in Software Industry through News Big Data

The purpose of this study is to identify issues related to software manpower, which became more important in the era of the Fourth Industrial Revolution in Korea. The results of this study can provide guidelines for those who establish software manpower training policies for solving the software industry’s human resource paradox. As for the research method, the quantitative text network and qualitative analyses from industry experts were used to interpret the results. A total of 14,752 news data mentioning software manpower were extracted, and data pre-processing for the synonyms and negative words were performed. The network was non-directional and consisted of 14,074 words (nodes) and 1,542,383 word combinations (edges). In addition, the network was clustered based on Modularity, and the degree of connection and eigenvector centrality were used to determine the importance of nodes. The analysis of the results showed that the government’s efforts through the Korean Ministry of Science and ICT were vital in creating jobs that fueled software innovation growth, and that software education was actively promoted to develop software talent. This study had the following implications. It was confirmed that software is making a high contribution to the expansion of business opportunities and job creation in the fields of new technology and software convergence technology. To resolve the software manpower supply-demand mismatch, it is necessary to cultivate high-quality software talent and provide mid- to long-term activities to attract competent human resources. In addition, it is necessary to develop and expand programs that link education and recruitment in terms of public-private cooperation along with government-led investment to strengthen national software competitiveness

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning

Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source domain datasets and then transferred to target domain data. Although the labeled datasets may provide suitable initial parameters for the target data, the domain difference between the source and target might hinder fine-tuning on the target domain. This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data. The re-randomization resets source-specific parameters of the source pre-trained model and thus facilitates fine-tuning on the target domain, improving few-shot performance.Comment: CIKM 2022 Short; 5 pages, 3 figures, 4 table

Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

Sensitization rates of airborne pollen and mold in children

PurposeAeroallergens are important causative factors of allergic diseases. Previous studies on aeroallergen sensitization rates investigated patients groups that had visited pediatric allergy clinics. In contrast, we investigated sensitization rates in a general population group of elementary school to teenage students in Incheon, Jeju, and Ulsan.MethodsAfter obtaining parental consent, skin-prick tests were performed on 5,094 students between March and June 2010. Elementary school students were tested for 18 common aeroallergens, whereas middle and high school students were tested for 25 allergens. The 25 allergens included Dermatophagoides pteronyssinus, Dermatophagoides farinae, pollen (birch, alder, oak, Japanese cedar, pine, willow, elm, maple, Bermuda grass, timothy grass, rye grass, orchard grass, meadow grass, vernal grass, mugwort, Japanese hop, fat hen, ragweed, and plantain), and mold (Penicillatum, Aspergillus, Cladosporium, and Alternaria).ResultsThe sensitization rates in descending order were 25.79% (D. pteronyssinus), 18.66% (D. farinae), 6.20% (mugwort), and 4.07% (willow) in Incheon; 33.35% (D. pteronyssinus), 24.78% (D. farinae), 15.36% (Japanese cedar), and 7.33% (Alternaria) in Jeju; and 32.79% (D. pteronyssinus), 30.27% (D. farinae), 10.13% (alder), and 8.68% (birch) in Ulsan. The dust mite allergen showed the highest sensitization rate among the 3 regions. The sensitization rate of tree pollen was the highest in Ulsan, whereas that of Alternaria was the highest in Jeju. The ragweed sensitization rates were 0.99% in Incheon, 1.07% in Jeju, and 0.81% in Ulsan.ConclusionThe differences in sensitization rates were because of different regional environmental conditions and distinct surrounding biological species. Hence, subsequent nationwide studies are required

BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202