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

RESEARCH ON NEARSHORE WAVE CONDITIONS AT NHAT LE COASTAL AREA (QUANG BINH PROVINCE) BY USING MIKE21-SW

Research on marine dynamics, including coastal wave motions, is a concern of countries in the world in general and Vietnam in particular. Coastal wave dynamics has a direct impact on human activities including coastal construction, shipping, irrigation, aquatic resources exploitation, etc. The coastal area of Nhat Le, Quang Binh is one of the areas strongly influenced by the coastal wave regime which increases the risk of coastal erosion, estuarine sedimentation, destroys the economic life, affects marine fishing and directly affects the tourist beach area. This article aims to introduce some research results based on the application of MIKE21-SW model of the Danish Hydraulic Institute (DHI) to simulate coastal wave regime in Nhat Le coastal zone, Quang Binh province. The model results are verified by real-time wave data in long-term from the WaMoS® II Radar System at Quang Binh station. The results show that there are many similarities in wave height and direction between the computational model and the actual observation data from the radar system. This result will be an important basis for research and application for coastal protection, reduction in river mouth sedimentation, clearing and flood drainage in the study area

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

The results of deep magnetotelluric sounding for studying the Nha Trang - Tanh Linh fault

The profile of deep magnetotelluric sounding (MT) from Duc Trong - Tuy Phong has been carried out in Lam Dong and Binh Thuan  provinces. The length of the Duc Trong - Tuy Phong profile is about 80 km with 15 stations and the distance between the stations measures about 5 km. Two-dimensional MT inversion was used to find a resistivity model that fits the data. The 2D resistivity model allows determining position and development formation of the Nha Trang - Tanh Linh  fault. This is the deep fault, which is showed by the boundaries of remarkable change of resistivity. In the near surface of the Earth (from ground to the depth of 6 km), the angle of inclination of this fault is about 60o; in the next part, the direction of the Nha Trang - Tanh Linh  faut is vertical. Geoelectrical section of the Nha Trang - Tanh Linh  profile shows that the resistivity of mid-crust is higher than that of lower-crust and of upper-crust

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

An active learning framework for duplicate detection in SaaS platforms

With the rapid growth of users’ data in SaaS (Software-as-a-service) platforms using micro-services, it becomes essential to detect duplicated entities for ensuring the integrity and consistency of data in many companies and businesses (primarily multinational corporations). Due to the large volume of databases today, the expected duplicate detection algorithms need to be not only accurate but also practical, which means that it can release the detection results as fast as possible for a given request. Among existing algorithms for the deduplicate detection problem, using Siamese neural networks with the triplet loss has become one of the robust ways to measure the similarity of two entities (texts, paragraphs, or documents) for identifying all possible duplicated items. In this paper, we first propose a practical framework for building a duplicate detection system in a SaaS platform. Second, we present a new active learning schema for training and updating duplicate detection algorithms. In this schema, we not only allow the crowd to provide more annotated data for enhancing the chosen learning model but also use the Siamese neural networks as well as the triplet loss to construct an efficient model for the problem. Finally, we design a user interface of our proposed deduplicate detection system, which can easily apply for empirical applications in different companies

Duplicate identification algorithms in SaaS platforms

Existing duplicate records is one of the most common issues in many Software-as-as-Service (SaaS) platforms. In this paper, we study the duplicate identification problem in one specific SaaS platform related to quality and compliance management by using the address information. We interpret all typical mistakes from users that can generate the existent duplicated organizations in a given dataset, collected from the SaaS platform. Also, we create another set by crawling location data from Open Address (US Zone). We compare different methods, including Bag-of-words (using Cosine Distance), Record Linkage Toolkits, and Siamese Neural Networks using the triplet loss, in terms of precision, recall, and F1-score. The experimental results show that using Siamese Neural Networks can achieve a better performance in comparison with other techniques. We plan to publish our Open Address dataset and all implementation codes to facilitate further research in the related fields

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