The thickness of the ventral medial prefrontal cortex (vmPFC) predicts the self-prioritization effect (SPE) on the interaction between spatial reference frames and spatial domains

Neuropsychological studies have demonstrated that the preferential processing of near-space and egocentric representation is associated with the self-prioritization effect (SPE). However, relatively little is known concerning whether the SPE is superior to the representation of egocentric frames or near-space processing in the interaction between spatial reference frames and spatial domains. The present study adopted the variant of the shape-label matching task (i.e., color-label) to establish an SPE, combined with a spatial reference frame judgment task, to examine how the SPE leads to preferential processing of near-space or egocentric representations. Surface-based morphometry analysis was also adopted to extract the cortical thickness of the ventral medial prefrontal cortex (vmPFC) to examine whether it could predict differences in the SPE at the behavioral level. The results showed a significant SPE, manifested as the response of self-associated color being faster than that of stranger-associated color. Additionally, the SPE showed a preference for near-space processing, followed by egocentric representation. More importantly, the thickness of the vmPFC could predict the difference in the SPE on reference frames, particularly in the left frontal pole cortex and bilateral rostral anterior cingulate cortex. These findings indicated that the SPE showed a prior entry effect for information at the spatial level relative to the reference frame level, providing evidence to support the structural significance of the self-processing region. The present study also further clarified the priority in SPE processing and role of the SPE within the real spatial domain.

From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies

According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes (Walsh, 2003).The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively.We clearly identified the presence of a system of brain regions commonly recruited in both space and time and that includes: bilateral insula, the pre-supplementary motor area (SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions.Our study supports but also expands the ATOM theory. Therefore, we here re-named it the ‘GradiATOM’ theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.

Authorized Deduplication of Encrypted data in Cloud

Cloud computing technology is rapidly developing nowadays. The number of files stored and processed is increasing per day. This increase brings severe challenge in requirement of space, processing power and bandwidth. More than half of the data generated in the cloud is duplicate data. To handle this data, deduplication technique is used which eliminates duplicate copies of data. This removal of duplicate data increases storage efficiency and reduce cost. In this paper, we propose secure role re-encryption system which allows authorized deduplication of data and also maintains privacy of data. This system is based on convergent algorithm and re-encryption algorithm that encrypts the user data and assign role keys to each user. This system grants privileges to users in order to maintain ownership of each user so that authorized users can access the data efficiently. In this system management center is introduced where the file is being encrypted and role keys are generated to handle authorized requests. Role keys are stored in Merkle hash tree which maps relationship between roles and keys. Authorized user who has particular role-encryption key can access the file. Convergent algorithm and role re-encryption algorithm allows access of specific file without leakage of private data. Dynamic updating of user privileges is achieved.

Methods of Image Retrieval Based Cloud

Content Based Image Retrieval (CBIR) introduces for the time being the most used system allowing detecting the visual features of images by using processing techniques due to the challenges of recovering photos depending on the text.The essential mechanism of content based image retrieval system is analysis picture information with low-level feature of an image, including colour, texture, determining edges, that has stages which includes the stage of classification of images, then the stage of extracting features and the stage of finding similar images and finally, evaluating the system and according to the technique or techniques used in each stage from stages of content based image retrieval system to increase the accuracy and speed of recovery for images ,together can using advantages cloud computing, which can provide a number of integrated computer services without being restricted to local resources, which include storage space, processing capabilities, expansion, flexibility and other various services through the Internet and deal with cloud through the platform as a user interface such as Microsoft Azure , Google Cloud platform, Amazon Web Services and others. In this paper, an analytical study of many CBIR techniques in terms of their behavior, feature extraction and work on Cloud Computing. All these techniques have their own avails as well as certain limitations, that means, there is not a one technique that suits superior all sorts of user’s needs,but using of advantages of cloud increase the improvement of performance. The focus be on the technique (features of Principle Component Analysis (PCA)) with advantages of Cloud Computing as a helpful way to extraction the features as shown in the results derived from the comparison.

A Review of Contemporary Image Compression Techniques and Standards

Uncompressed multimedia data such as images require huge storage space, processing power, transmission time, and bandwidth. In order to reduce the storage space, transmission time, and bandwidth, the uncompressed image data is compressed before its storage or transmission. This process not only permits a large number of images to be stored in a specified amount of storage space but also reduces the time required for them to be sent or download from the internet. In this chapter, the classification of an image on the basis of number of bits used to represent each pixel of the digital image and different types of image redundancies is presented. This chapter also introduced image compression and its classification into different lossless and lossy compression techniques along with their advantages and disadvantages. Further, discrete cosine transform, its properties, and the application of discrete cosine transform-based image compression method (i.e., JPEG compression model) along with its limitations are also discussed in detail.