Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

Mobile Networks and Applications - With the rapid development of Internet of things, the traditional city model is no longer applicable. Therefore, the emerging concept of smart city meets the...     

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

Importance of optical homogeneity for high-quality transparent ceramics

Two types of transparent Y₂O₃ ceramics without including large scattering sources such as residual pores, one with very high optical homogeneity (type A) and another one with slightly insufficient optical homogeneity (type B), are purposely prepared, and their optical properties are investigated and compared qualitatively and quantitatively. Type A ceramic exhibits transmittance characteristics with very low internal loss in the visible to infrared wavelength region, while type B ceramic is inferior in various optical performances especially in the short (visible) wavelength region. In type B ceramic, birefringence occurs due to optical inhomogeneity in the visible region, resulting in a decrease in the extinction ratio. Non-uniform refractive index distribution is also observed in the Schlieren image of type B ceramic, hence the laser beam quality through that material is degraded. This study proved the importance of optical homogeneity of transparent ceramics and clarified the problems in actual applications.     

Software Optimization in Ultrasound Imaging Technique Using Improved Deep Belief Learning Network on the Internet of Medical Things Platform

Wireless Personal Communications - The Internet of Medical Things (IoMT) is the array of medical instruments and related technologies that link Information Technology (IT) systems in...     

From ethics to standards – A path via responsible AI to cyber-physical production systems

The central claim of the paper is that the development and control of Cyber-Physical Production Systems (CPPS) requires a systematic approach to handle and include explicit ethical considerations. Since the contribution of artificial intelligence (AI) technologies, and of agent-based models in particular, was instrumental in the evolution of CPPSs, approaches of ethical AI should be endorsed in CPPS development by design. The paper discusses recent advances for ethical AI and suggests a pathway from ethical norms towards standards. As it is argued, taking the responsible AI approach is promising when tackling the main ethic-related challenges of Cyber-Physical Production Systems. We expose a number of dilemmas to be resolved so that AI systems incorporated in CPPS cause no damages either in humans, equipment or in the environment and increase the trust in the users of current and future AI technologies.     

Live video streaming service with pay-as-you-use model on Ethereum Blockchain and InterPlanetary file system

Wireless Networks - In centralized video streaming platforms, the platform owner, rather than the content producer, controls most of the content uploaded on the centralized video...     

Adaptive reversible data hiding for JPEG images with multiple two-dimensional histograms

Joint photographic experts group (JPEG) can provide good quality with small file size but also eliminate extensively the redundancies of images. Therefore, hiding data into JPEG images in terms of maintaining high visual quality at small file sizes has been a great challenge for researchers. In this paper, an adaptive reversible data hiding method for JPEG images containing multiple two-dimensional (2D) histograms is proposed. Adaptability is mainly reflected in three aspects. The first one is to preferentially select sharper histograms for data embedding after $K$ histograms are established by constructing the $k$th ($k\in \{1,2,\dots ,K\}$) histogram using the $k$th non-zero alternating current (AC) coefficient of all the quantized discrete cosine transform blocks. On the other hand, to fully exploit the strong correlation between coefficients of one histogram, the smoothness of each coefficient is estimated by a block smoothness estimator so that a sharply-distributed 2D-histogram is constructed by combining two coefficients with similar smoothness into a pair. The pair corresponding to low complexity is selected priorly for data embedding, leading to high embedding performance while maintaining low file size. Besides, we design multiple embedding strategies to adaptively select the embedding strategy for each 2D histogram. Experimental results demonstrate that the proposed method can achieve higher rate–distortion performance which maintaining lower file storage space, compared with previous studies.     

Imitative Collaboration: A mirror-neuron inspired mixed reality collaboration method with remote hands and local replicas

Mixed reality can overlay and display 3D digital content in the real world, convey abstract concepts to users, and promote the understanding of complex tasks. However, the abstract graphics overlaid on the physical space may cause a certain cognitive load for local users and reduce the efficiency of collaboration. To improve the efficiency of remote collaboration, we conducted an elicitation study on assembly tasks, explored the user needs for collaboration, and defined the design goals of our remote collaboration method. Inspired by the mirror-neuron mechanism, we present an imitative collaboration method that allows local users to imitate the interaction behavior of remote users to complete tasks. We also propose a series of interaction methods for remote users to select, copy, and interact with the local point clouds to facilitate the expression of collaboration intentions. Finally, the results of a user study evaluating our imitative collaboration method on assembly tasks are reported, confirming that our method improves collaboration efficiency while reducing the cognitive load of local users.