A Survey on Deep Learning Pouyanfar, Samira; Sadiq, Saad; Yan, Yilin ...
ACM computing surveys,
09/2019, Letnik:
51, Številka:
5
Journal Article
Recenzirano
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build ...computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.
With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting ...different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.
Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, ...small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution Programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.
Current models of COVID-19 transmission predict infection from reported or assumed interactions. Here we leverage high-resolution observations of interaction to simulate infectious processes. ...Ultra-Wide Radio Frequency Identification (RFID) systems were employed to track the real-time physical movements and directional orientation of children and their teachers in 4 preschool classes over a total of 34 observations. An agent-based transmission model combined observed interaction patterns (individual distance and orientation) with CDC-published risk guidelines to estimate the transmission impact of an infected patient zero attending class on the proportion of overall infections, the average transmission rate, and the time lag to the appearance of symptomatic individuals. These metrics highlighted the prophylactic role of decreased classroom density and teacher vaccinations. Reduction of classroom density to half capacity was associated with an 18.2% drop in overall infection proportion while teacher vaccination receipt was associated with a 25.3% drop. Simulation results of classroom transmission dynamics may inform public policy in the face of COVID-19 and similar infectious threats.
In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic ...gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
The Japanese government has promoted a number of policies regarding air pollution control to regulate the maximum permissible limits of air pollution, including motor vehicles, industrial activities, ...and firecrackers exhausts, which can be established as a mechanism for monitoring air pollution. Studies of Japan's air pollution have been presented since the 1970's, but only few studies have focused on evaluating air quality trend by grey system, which can be used to calculate the grade of air quality. The study used cardinal grey relational grade and grey entropy to calculate the data of five major Japan's air pollution such as sulfur dioxide (SO2) carbon monoxide (CO), suspended particulate matter (SPM), Nitrogen dioxide (NO2), and photochemical oxidants (OX) from 2002 to 2011 for evaluating the air quality trend in Japan. The relation between achievement of air pollution elimination and outcome of air pollution reduction can be determined via evaluating air quality trend. The results of this study can demonstrate the trend of air pollution spread from 2002 to 2011 and also can be used to establish a permissible limit for each type of air pollution. Furthermore, this study can be a reference for further studies on evaluating air quality trend and also on air pollution control and environmental protection, as an ultimate goal.
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•Because economy progress around the world, many environmental pollution occurred.•Most countries conduct many researches to alleviate the environmental pollution.•Air pollution reduction can be evaluated via analyzing air quality trend.•This study aims to evaluate air quality trend of Japan by grey relational grade.
Pictures or videos captured from a low-altitude aircraft or an unmanned aerial vehicle are a fast and cost-effective way to survey the affected scene for the quick and precise assessment of a ...catastrophic event's impacts and damages. Using advanced techniques, such as deep learning, it is now possible to automate the description of disaster scenes and identify features in captured images or recorded videos to gain situational awareness. However, building a large-scale, high-quality dataset with annotated disaster-related features for supervised model training is time-consuming and costly. In this article, we propose a weakly supervised approach to train a deep neural network on low-altitude imagery with highly imbalanced and noisy crowd-sourced labels. We further make use of the rich spatiotemporal data obtained from the pictures and its sequence information to enhance the model's performance during training via label propagation. Our approach achieves the highest score among all the submitted runs in the TRECVID2020 Disaster Scene Description and Indexing (DSDI) Challenge, indicating its superior capabilities in retrieving disaster-related video clips compared to other proposed methods.
The prevalence of multimedia applications has drastically increased the amount of multimedia data. With the drop of the hardware cost, more and more mobile devices with higher capacities are now ...used. The widely deployed wireless LAN and broadband wireless networks provide the ubiquitous network access for multimedia applications. Provision of Quality of Service (QoS) is challenging in mobile ad hoc networks because of the dynamic characteristics of mobile networks and the limited resources of the mobile devices. The wireless network is not reliable due to node mobility, multi-access channel and multi-hop communication. In this paper, we provide a survey of QoS provision in mobile multimedia, addressing the technologies at different network layers and cross-layer design. This paper focuses on the QoS techniques over IEEE 802.11e networks. We also provide some thoughts about the challenges and directions for future research.
Glaucoma is the leading cause of irreversible but preventable blindness worldwide, and visual field testing is an important tool for its diagnosis and monitoring. Testing using standard visual field ...thresholding procedures is time-consuming, and prolonged test duration leads to patient fatigue and decreased test reliability. Different visual field testing algorithms have been developed to shorten testing time while maintaining accuracy. However, the performance of these algorithms depends heavily on prior knowledge and manually crafted rules that determine the intensity of each light stimulus as well as the termination criteria, which is suboptimal. We leverage deep reinforcement learning to find improved decision strategies for visual field testing. In our proposed algorithms, multiple intelligent agents are employed to interact with the patient in an extensive-form game fashion, with each agent controlling the test on one of the testing locations in the patient's visual field. Through training, each agent learns an optimized policy that determines the intensities of light stimuli and the termination criteria, which minimizes the error in sensitivity estimation and test duration at the same time. In simulation experiments, we compare the performance of our algorithms against baseline visual field testing algorithms and show that our algorithms achieve a better trade-off between estimation accuracy and test duration. By retaining testing accuracy with reduced test duration, our algorithms improve test reliability, clinic efficiency, and patient satisfaction, and translationally affect clinical outcomes.
Homophily, the tendency for individuals to preferentially interact with others similar to themselves is typically documented via self-report and, for children, adult report. Few studies have ...investigated homophily directly using objective measures of social movement. We quantified homophily in children with developmental disabilities (DD) and typical development (TD) using objective measures of position/orientation in preschool inclusion classrooms, designed to promote interaction between these groups of children. Objective measurements were collected using ultra-wideband radio-frequency tracking to determine social approach and social contact, measures of social movement and interaction. Observations of 77 preschoolers (47 with DD, and 30 TD) were conducted in eight inclusion classrooms on a total of 26 days. We compared DD and TD groups with respect to how children approached and shared time in social contact with peers using mixed-effects models. Children in concordant dyads (DD-DD and TD-TD) both moved toward each other at higher velocities and spent greater time in social contact than discordant dyads (DD-TD), evidencing homophily. DD-DD dyads spent less time in social contact than TD-TD dyads but were comparable to TD-TD dyads in their social approach velocities. Children's preference for similar peers appears to be a pervasive feature of their naturalistic interactions.
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