Brain science, as an important branch of Neuroscience, is a discipline that studies the structure and function of brain nervous system of human and other mammals. With the invention of new technologies such as brain imaging, light microscopes and brain electromagnetics, brain science is gradually unraveling the mysteries of human emotion, intelligence and behavior. In this wave, data visualization punctuates the landmark advances in brain science since its beginning. This survey reviews the recent literature on brain network visualization (aka connectome) from the fields of both connectomics and visualization. In particular, we focus on the macroscopic-level brain network visualization techniques, that reveal the structural and functional connectivity of the whole brain, in comparison to microsopic-level neuronal connectivities. We also discuss the interactive visualization tools currently available for viewing the brain networks. Finally, we conclude with a number of ongoing challenges in macroscopic brain network visualization.

Increasing scale leaves a challenging problem for visualizing large attributed networks. This paper proposes a details on demand approach for exploratory visual analysis on large attributed networks. Major structures are located and emphasized at each level, providing clues for user observation. The detailed subnet structure emerges gradually through the exploration process. Our method dynamically aggregates network with consideration of both structural and attribute properties. It allows a flexible control of the hierarchy structure. A userspecified interaction strategy is introduced to enable users to customize the analysis flow according to different analytic tasks. Case studies demonstrate that the proposed method is effective in extracting global knowledge, locating major structures, and discovering hidden information in networks.

Based on a university's practice in upgrading its network management platform, presents a visual analytic system which integrates network topological space, IP space and network geographical space into a collaborative solution to help network administrators address difficulties in locating end user and troubleshooting. Throughout the development cycle, we worked alongside with users to clarify their actual demands and habitual operations through scenario application, interviews and a variety of evaluation. This user-centered approach guides us step by step to apply cyber security visualization and visual analytic technology to actual use.

A visual analytics system is proposed to reveal the lead/lag correlation when air pollution is detected. In this system, an Overview + Detail approach is utilized for analyzing the correlation of air quality under both the spatial and temporal dimensions and diffierent spatial-temporal scales. An annular container is proposed to preserve the context spatial information while the zoom level of the map changes. Based on the annular container, several analysis techniques such as STL decomposition view and correlation algorithm are integrated.

Spatial visualization has always been a primary part of information visualization and analysis, especially in the era of big data. The map, the most fundamental components of spatial visualization, is a kind of simple, intuitive and popular way to show the visualization of geographic information. The traditional map is not convenient to overlay complex elements due to its own complex filled color and the actual geographical boundaries. We aim to cut offi dusty foliage of the maps, and deliver the main structure of the map visualization result. We proposes RectMap, a boundary-reserved map deformation approach for visualizing geographical map, which can maintain the mind map of original map. The proposed approach integrate traditional Douglas-Peucker algorithm and our Gridding algorithm. The Douglas-Peucker algorithm generates a simplified map, and the Gridding algorithm optimizes the initial simplified map. Case study and user study are further conducted to demonstrate the effiectiveness and usefulness of the new-style map.

Congestion analysis is essential to traffic control, especially in crowded urban road network. The recent traffic forecasting methods can provide travelers and traffic managers with early congestion warning, yet unable to reveal the relationship of congestion roads. This paper presented a congestion propagation path estimation method based on greedy algorithm to quickly extract these congestion relationships for visual analytics. The data from traffic cameras are applied to build the propagation network based on a directed weighted graph. It describes the process of congestion spreading among different segments. According to this network, congestion propagation path predicts the process of congestion spreading between different segments. In our visual design, it is applied to demonstrate the segments that will be influenced by the congested road. This is helpful for traffic managers to make effective and efficient decisions. The experimental result shows that our method achieves high accuracy thus prove the effective for the congestion propagation method.

Traffic jam has become a severe urban problem to most metropolises in the world. How to understand and resolve these traffic problems has become a global issue. In the new era of big data, visualization and analysis with traffic-related data are increasingly appreciated. This paper presents DiffusionInsighter, a web-based visual traffic analysis system, that allows users to explore the traffic flow and diffusion patterns with different spatial and temporal granularity. The DiffusionInsighter first applies a visual data cleaning and filtering component to remove dirty data and remain available ones for further analysis. A set of carefully designed interaction and visualization tools including geographical view, pixel map view, chord diagram and network diffusion view is proposed in the DiffusionInsighter to support level-of-detail exploration of diffusion patterns of the traffic flow. Different views are collaborated together and are integrated into geographic map. A series of real-life case studies are conducted using a large GPS trajectory dataset of taxis in Hangzhou.

Understanding market trends and forming competitive promotion strategies has always been a major task of retail store managers. One big challenge is the lack of effiective tools for in-depth customer behavior analysis. In this paper, we apply visual analytics techniques to address the challenge, which is built up on the emerging and mobile location big data. We present a system that focuses on the analysis of customer stickiness which represents customers' affinity to retail stores. The system integrates mobile data pre-processing, customer stickiness analysis, multi-view visualization, and a set of interactions. The visual analytics techniques are mainly designed for two types of user tasks: 1) understanding the spatio-temporal distribution of customer traces related to retail stores; 2) evaluating the performance and trend of multiple retail stores through visual comparison. We have demonstrated the effiectiveness of the system through two case studies including advertisement placement and business branch reconfiguration.

In-vivo studies of fibrous structures require non-invasive tools, of which one is fiber tracking based on Diffiusion tensor imaging (DTI) datasets. Different fiber models can be produced from diffierent DTI images, which may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noises. Additionally, parameters of the tracking method also have a great influence on resulting models. Illustrating, exploring, and analyzing diffierences among DTI fiber models are crucial for the purposes of group comparison, atlas construction, and uncertainty analysis. Conventional approaches illustrate fiber models in 3D space and explore diffierences either voxel-wisely or fiber-based. However, these approaches rely on accurate alignment processes and may easily be disturbed by visual clutters. We introduce a two-phase projection technique to illustrate a complex 3D fiber model with a unique 2D map to characterize features for further exploration and analysis. Moreover, regions of significant diffierences among the maps are marked out. In these 2D maps, diffierences can be easily distinguished without occlusions that often occur in 3D spaces. To facilitate comparative analysis from multiple perspectives, we design an interface for interactive exploration. The effiectiveness of our approach is evaluated with two datasets.

A hybrid neuro-fuzzy model for predicting crime in a wide area such as a town or district in presented. The model is built using what we describe as crime indicator events extracted from simulated wide area surveillance network. The framework principally involves two phases, namely video analysis and crime modeling phases. In video analysis a concept based approach for video event detection is used to detect crime indicator events. Based on the extracted indicators with other related variables, a fuzzy inference system capable of learning is constructed in the second phase. The model is constructed using Violent scene detection (VSD) 2014 dataset and testing is done using UCR-Videoweb dataset. The experimental results show that the proposed method is quite demonstrative and promising.

This paper presents a method to derive optimal viewpoints for flying robot based transmission tower inspection, which applies point cloud model. A safe envelope is established according to safe transmission tower inspection rules. Essential inspection factors are proposed to evaluate the quality of candidate viewpoints, which includes visibility, dimensionality feature of point cloud as well as the distance between viewpoint and transmission power. A score function is constructed to quantify candidate viewpoints, while multiple attribute decision theory is applied to calculate the weight of each factor. Particle swarm optimization(PSO) is used to find the optimal viewpoints set. Both simulation experiment and practical observations are carried out. The results prove that optimal viewpoints have a great contribution for accurate transmission tower inspection. Final results are compared to patch-based method and proved to be feasible.

High performance of GPGPU comes from its super massive multithreading, which makes it more and more widely used especially in the field of throughputoriented. Data locality is one of the important factors affecting the performance of GPGPU. Although GPGPU can exploit intra/inter-warp locality by itself in part, there is still large improvement space for that. In our work, we analyze the characteristics of different applications and propose memory request based warp scheduling to better exploit inter-warp spatial locality. This method can make some warps with good inter-warp locality run faster, which is beneficial to improve the whole performance. Our experimental results show that our proposed method can achieve 24.7% and 11.9% average performance improvement over LRR and MRPB respectively.

A new characterization of balanced rotation symmetric (n, m)-functions is presented. Based on the characterization, the nonexistence of balanced rotation symmetric (p ^r , m)-functions is determined, where p is an odd prime and m ≥ 2. And there exist balanced rotation symmetric (2 ^r , m)-functions for 2 ≤ m ≤ 2 ^r − r. With the help of these results, we also prove that there exist rotation symmetric resilient (2 ^r , m)-functions for 2 ≤ m ≤ 2 ^r − r − 1.

Signcryption can realize encryption and signature simultaneously with lower computational costs and communication overhead than those of the traditional sign-then-encrypt approach. Certificateless cryptosystem solves the key escrow problem in the identity-based cryptosystem and simplifies the public key management in the traditional public key cryptosystem. So far there have been some certificateless signcryption schemes proposed in the standard model. However, they are either insecure or inefficient. They need long system public parameters, making it hard to deploy them in the limited storage environments. Based on the Gentry's identity-based encryption scheme, the authors propose a certificateless signcryption scheme in the standard model. Compared with previous schemes, the proposed scheme has not only much higher computational efficiency, but also shorter public parameters. The authors also give rigorous proof of its security.

The paper presents a fully integrated multiphase output low-jitter CMOS phase-locked loop for 1.25Gb/s to 6.25Gb/s wireline SerDes transmitter clocking. The self-biased bandwidth technology with simplified structure is applied to reduce the sensitivity to process variations. A diffierential Charge pump (CP) which is suitable for low power supply and process migration is proposed. An accelerator is built to avoid the disadvantage of great damping factor. Self-adaptive frequency dividers are used to improve power efficiency. The simulation results under 65nm and 55nm process almost maintain almost the same jitter performance and show the high process insensitivity and good jitter performance.

Assuming that misclassification costs between diffierent categories are equal, traditional Graph based semi-supervised classification (GSSC) algorithms pursues high classification accuracy. In many practical problems, especially in the fields of finance and medicine, compared with global classification accuracy, less cost on global misclassification is more likely to be the most significant factor. We propose one novel cost-sensitive classification algorithm based on the local and global consistency, which utilizes the semi-supervised classification algorithms better, and ensures higher classification accuracy on the basis of reducing overall cost. Our improved algorithm may bring some problems due to unbalanced data account, so we introduce synthetic minority oversampling technique algorithm for further optimization. Experimental results of bank loans and medical problems verify the effiectiveness of our novel classification algorithm.

Upon the fairness, security and flexibility problems of traditional digital rights management, we proposed a blockchain infrastructure service based DRM platform with high-level credit and security, in which we first proposed a Blockchain as a service (BaaS) architecture to decrease the complexity and difficulty of building up a blockchain-based business model, the BaaS infrastructure transparently provides easily-developed user interface to implement all core functions such as genesis block creation, consensus mechanism definition, node initialization and running, wallet management, address management, blockchain explorer et al. Then based on the BaaS infrastructure we proposed a blockchain-based DRM platform with high-level credit and security for Content provider (CP), Service provider (SP) and customers. We designed the blockchain as infrastructure service for DRM business model and provided core content rights information storage in blockchain for tamper-resistant copyrights protection from being misused, and the content consumers can use blockchain-based digital assets for content consumption payment, and the platform can help the content demandside and supply-side trading and the blockchain recorded the trade data as tamper-resistant evidence. Evaluation experiments manifests the proposed scheme is reliable and secure, and provided an efficient methodology for blockchain application business model implementation.

We introduce the concepts of Relevancematrix (RM) and Relevance-set (RS). And we construct the association between RM and the Knowledge compilation (KC) methods based on Extension rule (ER). Based on the basic parameters of RS and the relationship between RM and the KC methods based on ER, we design two efficient heuristics, called M2S (maximum sum of elements in RS and sum of literals in RS) and MNE (minimum number of maximum terms not extended by RS). Both of above heuristics intend to find the minimum set of maximum terms which cannot be extended by RS. Furthermore, we apply M2S and MNE on KCER. M2S_KCER (KCER with M2S) and MNE_KCER (KCER with MNE) are designed and implemented based on M2S and MNE, respectively. Experimentally, for the SAT instances with random lengths of clauses, M2S_KCER and MNE_KCER can improve the efficiency and quality of KCER sharply, and they are two best KC algorithms of EPCCL (each pair contains complementary literal) theory in all KC algorithms based on KCER.

Breast, ovarian and endometrial cancer are three most prevalent gynaecological malignancies. Identifying their common and specific biomarkers is significant for cancer prediction and therapy in females. We propose a method to identify dysregulated pathways in cancer through scoring pathways based on the molecular interaction data and genomic data. Commonly and specifically dysregulated pathways are analyzed across the above three female cancers, which have not been studied as a whole to the best of our knowledge. Our results demonstrate that all the three cancers have close relationships with Type II diabetes and cell cycle-related biology processes. Breast cancer is specifically related to immune system while ovarian cancer and endometrial cancer are associated with blood vascular-related systems such as renin-angiotensin system and coagulation system. In addition, dysregulated pathways are used to predict potential driver genes effiectively according to their topological structure and biological information.

We address the problem of filtering image spam, a kind of rapidly spread spam in which the text is embedded into images to defeat text-based spam filter. Particularly, we focus on image spam with Chinese text as “spam” which is a more challenging task. A popular way to detect image spam is by Optical character recognition (OCR) system, which detects and recognizes the embedded text, then followed by a text classifier that discriminate spam from ham. However, spammers start to obscure image text to prevent OCR system discovering the spam text. To compensate for the shortcomings of OCR system, a novel method which essentially is a keyword reconstruction algorithm based on Word activation force (WAF) model is proposed. It is effiective on discovering keywords, hence is benefit for the later classification stage and notably improve the performance of image spam filtering. The experimental results on a personal data set of spam images (publicly available) validate the effiectiveness of our approach that outperforms the original OCR system in practical usage with complex background in image spam.

Traditional Block compressed sensing (BCS) schemes encode nature images via a fixed sampling rate without taking the sparsity level diffierences among the blocks into consideration. In order to improve the sampling efficiency, a permutation-based BCS scheme with separate reconstruction is considered in this paper. The error performance bound of BCS scheme is carefully analyzed, and it is revealed that the smaller the maximum block sparsity level of the 2D signal is, the better reconstruction performance the algorithm has. According to the theoretical analysis result, an interweaving-permutationbased BCS strategy is investigated. In the proposed approach, the maximum block sparsity level of the 2D signal can be reduced significantly by interweaving permutation. As a result, better reconstruction performance can be achieved. Simulation results show that the proposed approach improves the Peak signal-to-noise ratio (PSNR) of reconstructed-images significantly.

A new Non-negative matrix factorization (NMF) based algorithm is proposed for single-channel speech separation with a prior known speakers, which aims to better model the spectral structure and temporal continuity of speech signal. First, NMF and k -means clus- tering are employed to obtain multiple small dictionaries as well as a state sequence that describes the temporal dynamics between these dictionaries for each speaker. Then, a Factorial conditional random field (FCRF) model is trained using the state sequences and dictionaries to jointly model the temporal continuity of two speakers' mixed signal for separation. Experiments show that the proposed algorithm outperforms the baselines with respect to all metrics, for example sparse NMF (+1.12dB SDR, +2.37dB SIR, +0.40dB SAR, +0.2 MOS), nonnegative factorial hidden Markov model (+2.04dB SDR, +4.26dB SIR, +0.62dB SAR, +1.0 MOS) and standard NMF (+2.8dB SDR, +5.08dB SIR, +1.06dB SAR, +1.2 MOS).

A variant of Grey wolf optimizer (GWO), called grey wolf optimizer with Ranking-based mutation operator (RGWO) is applied to the Infinite impulse response (IIR) system identification problem. RGWO makes GWO faster and more robust. In RGWO, the rankingbased mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.

Color shift keying (CSK) is the only Multiple-input multiple-output (MIMO) modulation scheme supported by IEEE 802.15.7 standard. It modulates the intensity of visible lights emitted by colored Lightemitting-diodes (LEDs) for data transmission. We discuss diffierent demodulation criteria for CSK in either signal space or color space, and compare their Bit-error rate (BER) performance. In order to improve the performance of CSK, we present a coded CSK modulation scheme, which is based on the recently proposed coding scheme called Block Markov superposition transmission (BMST). For comparison, we consider Reed-Solomon (RS) code together with CSK (referred as RS-CSK), which is exploited in PHY III operation mode in IEEE 802.15.7 standard. The mutual information of CSK is also given for performance analysis. Simulation results show that the proposed scheme achieves a significant gain compared with the RS-CSK.

The terminals in mobile cloud computing have themselves the special characteristics, such as mobile flexibility, distributed in different domains, resourceconstraint and easily to be captured et al. The terminals in mobile cloud computing participated the collaborative computing, information exchange and sharing secrets may come from different domains, different networks or different clouds. For this complex network environment the paper proposes a Multi-domain lightweight Asymmetric group key agreement (ML-AGKA). It adopts the bilinear mapping and blind key technology to achieve an asymmetric group key agreement protocol among mobile terminals distributed in different domains, proposes a computation and communication migration technologies to ensure that the mobile terminals are lightweight computing and communication consumption. The protocol can also achieve anonymity and authentication. The protocol is proven secure under the Computational Diffe-Hellman (CDH) problem assumption and the performance analysis shows that the proposed protocol is highly efficient.

In high-speed scenarios, channel model is of vital importance in Long term evolution (LTE) systems. By extensive measurements on Beijing-Tianjinrailway, this paper proposes a distance-dependent channel model. Both large-scale and small-scale channel characteristics are presented in detail. The large-scale path loss is modeled under the hierarchical network structure. Small-scale channel characteristics that include channel impulse response, power delay profile and Doppler spectrum are deduced and analyzed. In particular, a new stage-wise K-factor model is proposed. Combined with the cumulative distribution function of Root-mean squared (RMS) delay spread and the number of paths, they clearly illustrate the distancedependent variances of the channel. Finally, the integrated distance-dependent channel models are proposed to give a comprehensive description of broadband LTE channel in high-speed railway scenario.

Uplink cellular networks are usually modeled using simple Wyner-type cellular models where interference is simplified as a single random variable, or via 2-D Poisson point process (PPP) theory, with mobile users either scattered randomly or placed deterministically. These models are insufficient to evaluate performance in dense urban environments where a large number of small cells are installed. We take a fresh look at this problem using tools from 3-D PPP, and we develop a new general model based on 3-D space for uplink cellular networks. The main idea is modeling mobile users and small cells as two separate spatial PPPs. Under general assumptions, the uplink coverage probability can be easily evaluated through fast integral calculation. We compare our model to the traditional 2-D model and actual mobile user/small cell deployment, and we observe that the proposed model is more accurate and provides a closer bound of coverage probability.

In social networks, diffierent users may have diffierent privacy preferences and there are many users with public identities. Most work on diffierentially private social network data publication neglects this fact. We aim to release the number of public users that a private user connects to within n hops, called n-range Connection fingerprints (CFPs), under user-level personalized privacy preferences. We proposed two schemes, Distance-based exponential budget absorption (DEBA) and Distancebased uniformly budget absorption using Ladder function (DUBA-LF), for privacy-preserving publication of the CFPs based on Personalized diffierential privacy (PDP), and we conducted a theoretical analysis of the privacy guarantees provided within the proposed schemes. The implementation showed that the proposed schemes are superior in publication errors on real datasets.