Data Mining - Concepts and Techniques.

Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in the action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

Human Action Recognition and Prediction: A Survey.

Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits.

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Hiding images in plain sight: deep steganography

Watermarking and authentication of quantum images based on restricted geometric transformations

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management Resource management is a major problem especially for scientific workflows due to their heavy calculations and dependency between their operations Several algorithms and methods have been developed to manage cloud resources In this paper, the combination of state-action-reward-state-action learning and genetic algorithm is used to manage cloud resources At the first step, the intelligent agents schedule the tasks during the learning process by exploring the workflow Then, in the resource provisioning step, each resource is assigned to an agent, and its utilization is attempted to be maximized in the learning process of its corresponding agent This is conducted by selecting the most appropriate set of the tasks that maximizes the utilization of the resource Genetic algorithm is utilized for convergence of the agents of the proposed method, and to achieve global optimization The fitness function that has been exploited by this genetic algorithm seeks to achieve more efficient resource utilization and better load balancing by observing the deadlines of the tasks The experimental results show that the proposed algorithm reduces makespan, enhances resource utilization, and improves load balancing, compared to MOHEFT and MCP, the well-known workflow scheduling algorithms of the literature

Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm

Application of GRNN neural network in non-texture image inpainting and restoration

Capacity is one of the most important parameters in image watermarking. Different works have been done on this subject with different assumptions on image and communication channel. However, there is not a global agreement to estimate watermarking capacity. In this paper, we suggest a method to find the capacity of images based on their complexities. We propose a new method to estimate image complexity based on the concept of Region Of Interest (ROI). Our experiments on 2000 images showed that the proposed measure has the best adoption with watermarking capacity in comparison with other complexity measures. In addition, we propose a new method to calculate capacity using proposed image complexity measure. Our proposed capacity estimation method shows better robustness and image quality in comparison with recent works in this field.

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Estimating watermarking capacity in gray scale images based on image complexity

Today, the number of available videos on the Internet is significantly increased. Content-based video retrieval is used for finding the users' desired items among these big video data. Memorizing details of the videos and intricate relations between included objects in videos can be considered as the major challenges of this big data topic. A large portion of video data relates to the humans. Thus, human action retrieval has been introduced as a new big data topic that seeks to find video objects based on the included human action. Human action retrieval has been applicated in different domains such as video search, intelligent human---computer interaction, robotics, video surveillance and human behavior analysis. There are some challenges such as variations in rotation, scale, style and above-mentioned challenges for the big video data that can impress the retrieval accuracy. In this paper, a survey on human action retrieval studies is presented that the methodologies have been analyzed from action representation and retrieving perspectives. Moreover, limitations and common datasets of human action retrieval are introduced before describing the state-of-the-arts' methodologies.

A review on human action analysis in videos for retrieval applications

Due to different heterogeneous cloud resources and diverse and complex applications of the users, an optimal task scheduling, which can satisfy users and cloud service providers with energy-saving and cost-effective use of resources, is a major issue in cloud computing. On the one hand, network users are demanding the quality assurance of their requested services, minimizing their costs, and their own data security, and on the other hand, the service providers consider less power consumption, more efficient use of resources, and optimal utilization. In dependent tasks dealing with massive data, resource scheduling is considered as an important challenge. Due to the time limitation of online scheduling process of dependent tasks, many existing methods of the literature are not able to guarantee the best resource utilization. In this paper, a reinforcement learning approach is exploited in a multi-agent system for task scheduling and resource provisioning, in order to reduce the makespan, minimize the required power, optimize the cost of using the resources, and maximize the utilization of the resources (considering their expiration time), simultaneously. The proposed algorithm has two phases. In the first phase, the tasks are scheduled using reinforcement learning techniques, and in the second one, considering the information obtained from the scheduling phase, resources are allocated in a multi-agent environment. The results of experiments show that this method improves the efficiency of the use of resources and reduces their costs. Moreover, the expiration time of the tasks is observed and the total execution time and energy consumption will be significantly reduced.

Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents

Farzin Yaghmaee

Papers

Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm

Application of GRNN neural network in non-texture image inpainting and restoration

Estimating watermarking capacity in gray scale images based on image complexity

A review on human action analysis in videos for retrieval applications

Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents