Super-resolution: a comprehensive survey

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.     

Text prediction systems: a survey

Text prediction is one of the most widely used techniques to enhance the communication rate in augmentative and alternative communication. Prediction systems are traditionally used by people with disabilities (e.g. people with motor and speech impairments). However, new applications, such as writing short text messages via mobile phones, have recently appeared. A vast amount of heterogeneous text prediction methods and techniques can be found in literature. Their heterogeneity makes it difficult to understand and compare them, in order to select the most convenient technique for a specific design. This paper presents a survey on text prediction techniques with the intention to provide a systematic view of this field. Prediction applications and related features, such as block size, dictionary structure, prediction method, user interface, etc., are examined. In addition, prediction measurement parameters and published results are compared. A large number of factors that may influence prediction results, including the acceptance of the system by the users, are reviewed, and their influence on the performance and usability of the system is discussed.

Mobile botnet detection: a comprehensive survey

International Journal of Information Security - The number of people using mobile devices is increasing as mobile devices offer different features and services. Many mobile users install various...     

偏相关方法在软件缺陷预测中的应用*

为了提高预测模型的性能,解决不同属性子集带来的分歧,提出了基本偏相关方法的预测模型。首先,该方法在公开数据集上分析出代码静态属性与缺陷数之间存在偏相关关系;然后基于偏相关系数值,计算出代码复杂性度密度属性值;最后基于该属性值建立新的缺陷预测模型。实验表明,该模型具有较高的召回率和很好的F-measure性能,从而进一步证实了代码属性与模块缺陷之间的偏相关性是影响软件质量预测性能的重要因素的结论。该结论有助于建立更加稳定可靠的软件缺陷预测模型。     

Bike sharing usage prediction with deep learning: a survey

Neural Computing and Applications - As a representative of shared mobility, bike sharing has become a green and convenient way to travel in cities in recent years. Bike usage prediction becomes...     

Video content authentication techniques: a comprehensive survey

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.     

A survey on cancer prediction and detection with data analysis

Innovations in Systems and Software Engineering - World Health Organization reports cancer as a leading cause worldwide in mortality and morbidity. Accurate and early cancer risk assessment in...     

Reinforcement learning in robotic applications: a comprehensive survey

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.

     

FPGA-based accelerator for object detection: a comprehensive survey

The Journal of Supercomputing - Object detection is one of the most challenging tasks in computer vision. With the advances in semiconductor devices and chip technology, hardware accelerators have...     

Online and off-line handwriting recognition: a comprehensive survey

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered     

Microgrid control: A comprehensive survey

Present researches in power and energy fraternity are driven towards the realization of smart grid (SG) technologies. Microgrids (MGs) being regarded as “elementary units” of SG, has undergone rigorous research for more than one and a half decade now. It provides an integration platform for microsources (MSs), loads, storage devices and power electronics (PE) converters at demand premises forming a system of systems i.e. SoSs architecture. MGs mostly operated in grid-tied mode but during an emergency, are capable of standalone operation. Stability and operational control during both the modes are of utmost concern. Involvement of several control functionalities viz. normalcy of voltage and frequency, optimal power sharing, islanding detection etc., had been adhered in the literature. In spite of that, the real world implementation has not been of significant extent, that too even is far from having ideal MG characteristics, eligible for commercial usage. These technical obstacles need to be identified and dealt with. This paper provides a comprehensive survey of different control aspects of MGs, broadly classified under four control strategies: centralized, decentralized, distributed and hierarchical frameworks. Each scheme is reviewed in detail w.r.t the principles behind, their applicability and performances. It also identifies several research gaps and future trends therein. The technical barriers for real scale application and their solutions are also briefly discussed. And finally, a discussion on different integrated technologies for MGs, to realize SG features is also presented.     

Postal address extraction from the web: a comprehensive survey

Artificial Intelligence Review - The Web is a source of information for Location-Based Service (LBS) applications. These applications lack postal addresses for the user’s Point of Interests...     

Deep reinforcement learning in computer vision: a comprehensive survey

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i) landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision.

     

Model-based performance prediction in software development: a survey

Over the last decade, a lot of research has been directed toward integrating performance analysis into the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach does not take into account the fact that performance problems may require considerable changes in design, for example, at the software architecture level, or even worse at the requirement analysis level. Several approaches were proposed in order to address early software performance analysis. Although some of them have been successfully applied, we are still far from seeing performance analysis integrated into ordinary software development. In this paper, we present a comprehensive review of recent research in the field of model-based performance prediction at software development time in order to assess the maturity of the field and point out promising research directions.     

Crowd analysis: a survey

In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.     

Towards building a universal defect prediction model with rank transformed predictors

Software defects can lead to undesired results. Correcting defects costs 50 % to 75 % of the total software development budgets. To predict defective files, a prediction model must be built with predictors (e.g., software metrics) obtained from either a project itself (within-project) or from other projects (cross-project). A universal defect prediction model that is built from a large set of diverse projects would relieve the need to build and tailor prediction models for an individual project. A formidable obstacle to build a universal model is the variations in the distribution of predictors among projects of diverse contexts (e.g., size and programming language). Hence, we propose to cluster projects based on the similarity of the distribution of predictors, and derive the rank transformations using quantiles of predictors for a cluster. We fit the universal model on the transformed data of 1,385 open source projects hosted on SourceForge and GoogleCode. The universal model obtains prediction performance comparable to the within-project models, yields similar results when applied on five external projects (one Apache and four Eclipse projects), and performs similarly among projects with different context factors. At last, we investigate what predictors should be included in the universal model. We expect that this work could form a basis for future work on building a universal model and would lead to software support tools that incorporate it into a regular development workflow.