Deep Learning for Mobile Multimedia: A Survey

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments

An Analysis of Shoreline Changes Using Combined Multitemporal Remote Sensing and Digital Evaluation Model

Cua Dai estuary belonged to Quang Nam province is considered to be one of the localities of Vietnam having a complex erosion and accretion process. In this area, sandbars are recently observed with lots of arguments about the causes and regimes of formation. This could very likely result of not reliable source of information on shoreline evolution and a lack of historical monitoring data. Accurately identification of shoreline positions over a given period of time is a key to quantitatively and accurately assessing the beach erosion and accretion. The study is therefore to propose an innovative method of accurately shoreline positions for an analysis of coastal erosion and accretion in the Cua Dai estuary. The proposed technology of multitemporal remote sensing and digital evaluation model with tidal correction are used to analyse the changes in shoreline and estimate the rate of erosion and accretion. An empirical formula is, especially, exposed to fully interpret the shoreline evolution for multiple scales based on a limitation of satellite images during 1965 to 2018. The results show that there is a significant difference of shoreline shift between corrections and non-corrections of tidal. Erosion process tends to be recorded in the Cua Dai cape located in the Cua Dai ward, especially in the An Luong cape located in the Duy Hai commune with the length of 1050 m. Furthermore, it is observed that there is much stronger erosion in the north side compared with south side of Cua Dai estuary

ieee access special section editorial multimedia analysis for internet of things

Big data processing includes both data management and data analytics. The data management step requires efficient cleaning, knowledge extraction, and integration and aggregation methods, whereas Internet-of-Multimedia-Things (IoMT) analysis is based on knowledge modeling and interpretation, which is more often performed by exploiting deep learning architectures. In the past couple of years, merging conventional and deep learning methodologies has exhibited great promise in ingesting multimedia big data, exploring the paradigm of transfer learning, association rule mining, and predictive analytics etc

A Framework for paper submission recommendation system

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers

Selective breeding of saline-tolerant striped catfish (Pangasianodon hypophthalmus) for sustainable catfish farming in climate vulnerable Mekong Delta, Vietnam

peer reviewedStriped catfish (Pangasianodon hypophthalmus), a freshwater species cultured mainly in the Mekong Delta region in Southern Vietnam, is facing a significant challenge due to salinity intrusion as a result of climatic changes. Given these evolving environmental conditions, selecting new strains with a higher salinity tolerance could make production of striped catfish economically feasible in brackish environments. In this study, we carried out a selection program aimed at developing a striped catfish strain able to survive and grow fast in a saline environment. To implement the selection program, we first collected males and females from different provinces in the Mekong delta. We next performed a factorial cross of these breeders to produce half- and full-sib families. When fish reached fry stage (47 dph), we put them in a saline environment (10 ppt) and subsequently kept 50 % of the fastest-growing fish after 143 days post hatching (dph). We repeated this mass selection procedure after 237 dph and 340 dph. We maintained in parallel a randomly selected group in saline conditions and a group of fish reared in freshwater to serve as controls. After crossing the selected individuals, we performed several tests on the next generation of fish to evaluate the effectiveness of selection after one generation in saline conditions. Average direct responses to selection were 18.0 % for growth and 11.4 % for survival rate after one generation of selection. We estimated a moderate realized heritability (0.29) for body weight. The genetic gains obtained in our study for body weight and survival rate after one generation of selection under saline conditions suggest that selection can be effective to improve ability of striped catfish to cope with saline stress. We conclude that our selection program has succeeded in developing a productive strain of striped catfish with better tolerance to salinity. © 2022 The Author

Synthesis of Silica-Coated Magnetic Nanoparticles and Application in the Detection of Pathogenic Viruses

Magnetic Fe3O4 nanoparticles were prepared by coprecipitation and then coated with silica. These Fe3O4/SiO2 nanoparticles consisted of a 10–15 nm magnetic core and a silica shell of 2–5 nm thickness. The superparamagnetic property of the Fe3O4/SiO2 particles with the magnetization of 42.5 emu/g was confirmed by vibrating sample magnetometer (VSM). We further optimized buffers with these Fe3O4/SiO2 nanoparticles to isolate genomic DNA of hepatitis virus type B (HBV) and of Epstein-Barr virus (EBV) for detection of the viruses based on polymerase chain reaction (PCR) amplification of a 434 bp fragment of S gene specific for HBV and 250 bp fragment of nuclear antigen encoding gene specific for EBV. The purification efficiency of DNA of both HBV and EBV using obtained Fe3O4/SiO2 nanoparticles was superior to that obtained with commercialized Fe3O4/SiO2 microparticles, as indicated by (i) brighter PCR-amplified bands for both HBV and EBV and (ii) higher sensitivity in PCR-based detection of EBV load (copies/mL). The time required for DNA isolation using Fe3O4/SiO2 nanoparticles was significantly reduced as the particles were attracted to magnets more quickly (15–20 s) than the commercialized microparticles (2-3 min)

Low birth weight of Vietnamese infants is related to their mother’s dioxin and glucocorticoid levels

We aimed to determine the relationship between dioxin congeners in maternal breast milk and maternal glucocorticoid levels with newborn birth weight after nearly 45 years of use of herbicides in the Vietnam War. The study subjects comprised 58 mother–infant pairs in a region with high dioxin levels in the soil (hotspot) and 62 pairs from a control region. Dioxin levels in maternal breast milk were measured by HRGC-HRMS. Salivary glucocorticoid levels were determined by LC-MS/MS. Dioxin congener levels in mothers from the hotspot were found to be two to fivefold higher than those in mothers from the control region. Birth weight was inversely correlated with 2,3,7,8-TeCDD and 2,3,4,7,8-PeCDF congener levels. The rate of newborns whose birth weight was less than 2500 g was threefold higher in the hotspot (12 %) than in the control region (4 %). Salivary glucocorticoid levels in mothers with low birth weight infants were significantly higher than those in the normal birth weight group. Low birth weight of Vietnamese newborns in a hotspot for dioxin levels is related to some dioxin congener levels and high glucocorticoid levels in mothers. This finding in mother–infant pairs suggests that excess maternal glucocorticoid levels are related to dioxin burden and they result in low birth weight. © 2016 Springer-Verlag Berlin HeidelbergEmbargo Period 12 month

Лексические средства связности в речи телеведущего в жанре интерактивной белорусскоязычной телебеседы

Материалы XII Междунар. науч. конф. студентов, магистрантов, аспирантов и молодых ученых, Гомель, 16–17 мая 2019 г