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The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social science, four elements are required to construct a sound crowd—Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three components have already been investigated and implemented in existing crowdsourcing platforms, 'Diversity of Opinion' has not been functionally enabled yet. From a computational point of view, constructing a wise crowd necessitates quantitatively modeling and taking diversity into account. There are usually two paradigms in a crowdsourcing marketplace for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. We propose similarity-driven and task-driven models for both paradigms. Also, we develop efficient and effective algorithms for recruiting a limited number of workers with optimal diversity in both models. To validate our solutions, we conduct extensive experiments using both synthetic datasets and real data sets. [ABSTRACT FROM AUTHOR]

Image-text retrieval aims to capture the semantic correspondence between images and texts, which serves as a foundation and crucial component in multi-modal recommendations, search systems, and online shopping. Existing mainstream methods primarily focus on modeling the association of image-text pairs while neglecting the advantageous impact of multi-task learning on image-text retrieval. To this end, a multi-task visual semantic embedding network (MVSEN) is proposed for image-text retrieval. Specifically, we design two auxiliary tasks, including text-text matching and multi-label classification, for semantic constraints to improve the generalization and robustness of visual semantic embedding from a training perspective. Besides, we present an intra- and inter-modality interaction scheme to learn discriminative visual and textual feature representations by facilitating information flow within and between modalities. Subsequently, we utilize multi-layer graph convolutional networks in a cascading manner to infer the correlation of image-text pairs. Experimental results show that MVSEN outperforms state-of-the-art methods on two publicly available datasets, Flickr30K and MSCO-CO, with rSum improvements of 8.2% and 3.0%, respectively. [ABSTRACT FROM AUTHOR]

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Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, θ -Iceberg Core decomposition in graphs, which is able to incorporate a parameter θ ( 0 < θ ≤ 1 ) used for relaxing the output of k-cores. Further, we propose a formal concept analysis based approach for θ -Iceberg Core decomposition. The proposed approach and its conclusions can provide theoretical basis and guidance for the potential applications of θ -Iceberg Core analysis in complex networks. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to prepare a stable irinotecan liposome (CPT-11 liposome) and evaluate its antitumor efficacy in a range of tumor models. Methods: CPT-11 liposome was prepared with a Z-average particle size of 110 ~ 120 nm and high entrapment efficiency (> 95%) and had a good stability within 18 months. Then the antitumor efficacy was studied in human colon (Ls-174t), gastric (NCI-N87), pancreatic (BxPC-3) and small cell lung (NCI-H526) cancer xenograft models. The toxicity of high-dose CPT-11 liposome was also evaluated in Beagle dogs. Results: The results showed that the anti-tumor effects of CPT-11 liposome were markedly superior (at least 10 times higher) to those of the CPT-11 injection group in all four xenograft models. The tissue distribution test in the Ls-174t model further demonstrated that the CPT-11 liposome could alter the plasma and tissue distribution of CPT-11, increase the exposure level of its active metabolite SN-38 in tumor, and ultimately improve antitumor efficiency. Meanwhile, CPT-11 liposome showed a much less toxicity than CPT-11 injection in beagle dogs. Conclusions: Overall, the CPT-11 liposome may be developed as a new clinical alternative for the cancer patients. [ABSTRACT FROM AUTHOR]

Background: Regarding trochanteric hip fractures, one type of posterior coronal fragments was described as the "banana-shaped fragment", while the impact of the banana-shaped fragment on mechanical stability has not been further studied. The current study investigated the association between the banana-shaped fragment and mechanical complications after surgery. Methods: This retrospective cohort study included 273 patients treated by proximal femoral nail antirotation (PFNA) in the full analysis. The age, the sex, the fracture side, the follow-up time, the American Society of Anesthesiologists classification, the operators, the fracture classification, the tip-apex distance, the blade positions, the reduction quality and the bone mineral density were analyzed in relation to mechanical complications, through univariate and multivariate approaches. Results: Mechanical complications happened in 33 patients. The banana-shaped fragment (adjusted odds ratio 5.240, 95% CI 2.172 to 12.641; p < 0.001), the tip-apex distance and the reduction quality showed significant association with mechanical complications in both univariate and multivariate analysis. Moreover, for 118 patients with the banana-shaped fragment, we found that the use of wire cerclage couldn't significantly lower the rates of mechanical complications (p = 0.648). Conclusions: The banana-shaped fragment had a negative impact on mechanical stability of trochanteric hip fractures treated by PFNA. In the perioperative period, the BSF should be carefully evaluated, and its specific handling deserves further study. [ABSTRACT FROM AUTHOR]

Information cascades are identified as a major factor in almost every disastrous social network phenomenon, ranging from viral marketing, rumor spread, cyber violence, and various negative information diffusion. A small fraction of nodes has an important impact on the spread of information, therefore, mining these influential nodes helps block information cascades. Most of the existing influential nodes identification relies on topological features, however, they fail to simultaneously capture network topology, user behaviors, and information content during the diffusion, which may result in unsatisfactory control performance. To this end, we develop an effective method for blocking information cascades, which is based on extended bridge nodes identification. Individual decision-making behaviors with similar tendencies contribute to information cascades, but tight clusters block the diffusion of cascades. Bridges with double-edged characters on social networks are powerful ways to propagate new information from different clusters, but they are weak at forwarding information that is in some way risky or costly to adopt forwarding behaviors need to be supported by a higher proportion of neighbors. Threshold models for diffusion and double-edged bridges have been used to motivate the definition of bridge_like nodes and the modeling of information cascades in this article. A blocking method is proposed to identify as few influential extended bridge nodes as possible in a network to maximize information cascades blocking, and finally, the performance of the blocking method is validated on social networks. [ABSTRACT FROM AUTHOR]

By incorporating three-way decision model into formal concept analysis (FCA) methodology, an emerging novel data analysis methodology, termed three-way concept analysis (3WCA), has been widely used in both computer science and social science areas. However, the construction of three-way concept lattice is quite time-consuming and proved as an NP-complete problem. Thus, it makes the knowledge discovery from three-way concept lattice difficult. To facilitate the knowledge acquisition in three-way concept lattice, both attribute reduction and three-way concept stability are utilized for pruning the size of three-way concept lattice and extracting informative three-way concepts. Aiming to extract hidden knowledge efficiently, this paper first attempts to figure out the relations of three-way concept stability in the original three-way concept lattice and reduced three-way concept lattice. We then propose a theorem on the invariance of three-way concept stability for attribute reduction of three-way concept lattice. To validate the correctness of our finding, we also conducted an empirical case study. [ABSTRACT FROM AUTHOR]

When using the Tsallis entropy method to segment light colored character images on the semiconductor chip surface, it consumes relatively long CPU time. In order to reduce the consumption of CPU time, a new threshold segmentation entropy is proposed on the basis of the Tsallis entropy, called arctangent entropy (Arctangent entropy).It introduces the arctangent operator and constructs a mathematical model that can effectively adjust the foreground entropy and background entropy. The foreground entropy and background entropy of the image are adjusted by adjusting the parameter k. Experiments show that compared with the Tsallis entropy, Kapur entropy, Renyi entropy, Minimum error threshold (MET) and Iterative threshold (IT),the CPU time consumed by the arctangent entropy is lowest. The total CPU time average consumed by the Kapur entropy is approximately 14.4 times that of the Arctangent entropy. The total CPU time average consumed by the MET is approximately 4.6 times that of the Arctangent entropy. The total CPU time average consumed by the Tsallis entropy, IT and Renyi entropy are approximately 2.7, 2.6, 2.5 times that of the Arctangent entropy, respectively. The arctangent entropy has the best segmentation effect on light colored character images. [ABSTRACT FROM AUTHOR]

In the field of artificial intelligence, a large number of promising tools, such as condition-based maintenance, are available for large internal combustion engines. The cylinder liner, which is a key engine component, is subject to defects due to the manufacturing process. In addition, the cylinder liner straightforwardly affects the usage and safety of the internal combustion engine. Currently, the detection of cylinder liner quality mainly depends on manual human detection. However, this type of detection is destructive, time-consuming, and expensive. In this paper, a new cylinder liner defect database is proposed. The goal of this research is to develop a nondestructive yet reliable method for quantifying the surface condition of the cylinder liner. For this purpose, we propose a transformer method with a block division and mask mechanism on our newly collected cylinder liner defect database to automatically detect defects. Specifically, we first use a local defect dataset to train the transformer network. With a hierarchical-level architecture and attention mechanism, multi-level and discriminative feature are obtained. Then, we combine the transformer network with the block division method to detect defects in 64 local regions, and merge their results for the high-resolution image. The block division method can be used to resolve the difficulty of the in detecting the small defect. Finally, we design a mask to suppress the influence of noise. All methods allow us to achieve higher accuracy results than state-of-the-art algorithms. Additionally, we show the baseline results on the new database. [ABSTRACT FROM AUTHOR]

Many models were recently proposed to classify students, relying on a large amount of pre-labeled data to verify their classification effectiveness. However, those models lack to accurately classify students into various behavioral patterns, employing nominal class labels, rather than ordinal ones. Meanwhile, such models cannot analyze high-dimensional learning behaviors among learners according to students' interaction with course videos. Since online learning data are huge, the main challenges associated with data are insufficient labeling and classification using nominal class labels. In this study, we proposed a model based on Graph Convolutional Network, as a semi-supervised classification task to classify students' engagement in various behavioral patterns. First, we proposed a label function to label datasets instead of manual labeling, in which input and output data are labeled for classification to provide a learning foundation for future data processing. Accordingly, we hypothesized four behavioral patterns, namely ("High-engagement", "Normal-engagement", "At-risk", and "Potential-At-risk") based on students' engagement with course videos and their performance on the assessments/quizzes conducted after. Then, we built a heterogeneous knowledge graph representing learners, course videos as entities, and capturing semantic relationships among students according to shared knowledge concepts in videos. Our model intrinsically works for heterogeneous knowledge graphs as a semi-supervised node classification task. It was evaluated on a real-world dataset across multiple settings to achieve a better predictive classification model. Experiment results showed that the proposed model can predict with an accuracy of 84% and an f1-score of 78% compared to baseline approaches. [ABSTRACT FROM AUTHOR]

Citation recommendation systems mainly help researchers find the lists of references that related to their interests effectively and automatically. The existing approaches face the issues of data sparsity and high-dimensional in large-scale bibliographic network representation, which hinder the citation recommendation performance. To address these problems, we proposed a Content-Sensitive citation representation approach for Citation Recommendation, named CSCR. Firstly, the Doc2vec model is used to generate a paper embedding according to paper content. Then, utilizing the similarity between the paper content embeddings to select the assumed neighbours of the target paper, append the auxiliary links between target paper and its new neighbours in the bibliographic network. Thirdly, distributed network representation method is implemented on appended bibliographic network to obtain the paper node embedding, which can learn interpretable lower dimension embedding for paper nodes. Finally, the embedding vectors of these papers can be used to conduct citation recommendation. Experimental results show that the proposed approach significantly outperforms other benchmark methods in Normalized Discounted Cumulative Gain (NDCG) and the positive rate (Recall). [ABSTRACT FROM AUTHOR]

A high-precision camera intrinsic parameters calibration method based on concentric circles was proposed. Different from Zhang's method, its feature points are the centers of concentric circles. First, the collinearity of the projection of the center of concentric circles and the centers of two ellipses which are imaged from the concentric circles was proved. Subsequently, a straight line passing through the center of concentric circles was determined with four tangent lines of concentric circles. Finally, the projection of the center of concentric circles was extracted with the intersection of the straight line and the line determined by the two ellipse centers. Simulation and physical experiments are carried out to analyze the factors affecting the accuracy of circle center coordinate extraction and the results show that the accuracy of the proposed method is higher. On this basis, several key parameters of the calibration target design are determined through simulation experiments and then the calibration target is printed to calibrate a binocular system. The results show that the total reprojection error of the left camera is reduced by 17.66% and that of the right camera is reduced by 21.58% compared with those of Zhang's method. Therefore, the proposed calibration method has higher accuracy. [ABSTRACT FROM AUTHOR]

Alangchnin A (1), a new isoflavonoid, together with two known compounds (2 and 3), was isolated from the roots of Alangium chinense. The structures of these compounds were established by HR-ESI-MS, and 1D and 2D NMR experiments. Due to the rotation of the flexible single bond, the absolute configuration of compound 1 was further elucidated using ECD quantum chemical calculations. [ABSTRACT FROM AUTHOR]

Building energy efficiency, as a traditional field which has been existing for decades performs a prosperous needs with diversity of corresponding methods. In the flow of artificial intelligence (AI) background, where does the building energy efficiency advance and how does it emphasize? This question seems to become more significant with the blueprints of zero energy building implementation issued by many countries. The major objective of this research is to review, analyze and identify the performance of AI based applications in buildings, especially for building energy efficiency and zero energy building. Based on the present research trends, the possible changes AI based approach brings to related laws, regulations and standards are firstly analyzed. The main aspects of the AI based approach infrastructure in buildings is thoroughly reviewed and compared. IoT based sensor applications for thermal comfort, platforms and algorithms for building multi energies control, and forecasting methods for building load, subsystem performance and structure safety are summarized. To provide more optimal references for zero energy building solutions, the AI based approach in zero energy building is then predicted in detail, with particular analysis of occupant presence and behaviors. Finally, the future directions of the research on AI based applications for zero energy building implementation are summarized. [ABSTRACT FROM AUTHOR]

The event-triggered bipartite consensus problem of linear multi-agent systems with a connected structurally balanced signed graph is considered in this paper. The state observer is set to estimate the actual states of the system. In the system structure, two event-triggered mechanisms are configured for each agent. The one is placed in the output side of the agent and the other in the controller. Thus the establishment of the state observer is based on the discrete output information sampled at the corresponding triggering time instants. And the inter-neighboring transmission of the observed states occurs at the moments when the corresponding triggering condition of the agent is satisfied. The triggering conditions designed do not rely on continuous inter-neighboring communications. With the help of the algebraic graph theory and Lyapunov stability theory, it is proved that the bipartite consensus can be achieved. Furthermore, Zeno behavior can be avoided in both sides. The effectiveness of the presented control strategy is demonstrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

Advertising is becoming a business on social networks. Billions of people around the world use social media, and fastly, it has become one of the defining technologies of our time. Social platforms like Twitter are one of the primary means of communication and information dissemination and can capture the interest of potential customers. Therefore, it is crucial to select suitable advertisements to users in specific times and locations for capturing their attention, profitably. In this paper, we propose a context-aware advertising recommendation system that, by analyzing the users' tweets and movements along a timeline, infers the personal interests of users and provides attractive ads to users through the triadic formal concept analysis theory. [ABSTRACT FROM AUTHOR]

The current big data era has placed new challenges for the study and application of information management (IM). The paper entitled "A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer's Disease Using Machine Learning Methods" [[8]], used a multi-task learning algorithm based on the ADNI dataset to predict the cognitive scores of subjects in the next three years. Also, the authors use four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, and thus to achieve the effect of assisting doctors in diagnosis. [Extracted from the article]

The increasing social problems on population, resources and environment enable the interaction between nature and humanity to become one of the most active research fields in the world. In this paper, we propose a novel framework of human–land sustainable computational system, which fully advances the progress of the development of our society utilizing the cloud computing and big data analysis technologies. Particularly, the study on quality of land management has attracted much attention. With the proposed framework, multi-user multi-cloud environment (MUMCE) is firstly presented, and evaluation of land quality is regarded as various services, such as soil acidity and alkalinity, soil thickness, soil texture, smoothness and field layout. Then, this paper formulates the problem of formal concept analysis-based multi-cloud composition recommendation with regard to multiple users. To address this problem, this paper first adopts the collaborative filtering to obtain the services request of the target user, then the service–provider concept lattices are constructed, and finally the best multi-cloud composition is selected and further recommended for the target user. Meanwhile, the corresponding algorithm is also devised. A case study is conducted for evaluating the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Network embedding technologies that transform the nodes of a network into a low-dimensional vector space have many various potential applications such as node classification, community detection and Internet public opinion analysis and so forth. Most existing approaches for network embedding are calculated by utilizing the topology structure of a target network which simply describes the social relations among nodes. However, another factor derived from the significant "opinions" is usually neglected by those works. In particular, there exist dramatic cases of Internet Public Affair, where many users with the same opinion have no social connections. On top of that, the social network can be very sparse, which is unsuitable for network embedding. Therefore, this paper proposes an efficient approach opinion feature network embedding (OFNE) that combines both social relations and opinion features within a network. OFNE defines an opinion feature edge to preserve the opinion features. Experimental results on real-world datasets across different domains demonstrate that the proposed approach OFNE outperforms the regular social network embedding approaches, especially when the opinions have explicit sentiment orientation. [ABSTRACT FROM AUTHOR]

Artificially improving traits of cultivated alfalfa (Medicago sativa L.), one of the most important forage crops, is challenging due to the lack of a reference genome and an efficient genome editing protocol, which mainly result from its autotetraploidy and self-incompatibility. Here, we generate an allele-aware chromosome-level genome assembly for the cultivated alfalfa consisting of 32 allelic chromosomes by integrating high-fidelity single-molecule sequencing and Hi-C data. We further establish an efficient CRISPR/Cas9-based genome editing protocol on the basis of this genome assembly and precisely introduce tetra-allelic mutations into null mutants that display obvious phenotype changes. The mutated alleles and phenotypes of null mutants can be stably inherited in generations in a transgene-free manner by cross pollination, which may help in bypassing the debate about transgenic plants. The presented genome and CRISPR/Cas9-based transgene-free genome editing protocol provide key foundations for accelerating research and molecular breeding of this important forage crop. Alfalfa is an important forage crop, but genetic improvement is challenging due to the lack of a reference genome and an efficient genome editing protocol. Here, the authors report the chromosome-level assembly of the autotetraploid genome and a CRISPR/Cas9-based transgene-free genome editing protocol. [ABSTRACT FROM AUTHOR]

This paper studies event-triggered control for disturbed nonlinear systems. A new dual-stage Lyapunov-based event-triggering condition is proposed to cope with the time-varying transmission delays. In the first stage, the ratio of Lyapunov function values at the last two triggering instants is calculated. Then based on the ratio, the corresponding threshold function is selected from two candidate forms. It is proved that the designed event-triggered control system is input-to-state practically stable with respect to the measurement errors and disturbances. Moreover, Zeno behavior is excluded successfully by calculating the lower bound of the minimum inter-event times. Finally, a simulation example is provided to show the feasibility and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The tremendous development of community detection in dynamic networks have been witnessed in recent years. In this paper, intimacy evolutionary clustering algorithm is proposed to detect community structure in dynamic networks. Firstly, the time weighted similarity matrix is utilized and calculated to grasp time variation during the community evolution. Secondly, the differential equations are adopted to learn the intimacy evolutionary behaviors. During the interactions, intimacy between two nodes would be updated based on the iteration model. Nodes with higher intimacy would gather into the same cluster and nodes with lower intimacy would get away, then the community structure would be formed in dynamic networks. The extensive experiments are conducted on both real-world and synthetic signed networks to show the efficiency of detection performance. Moreover, the presented method achieves better detection performance compared with several better algorithms in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

The size of underground openings in rock masses in metal mines is critical to the performance of the openings. In this study, the mechanical and acoustic emission (AE) characteristics of brittle rock-like specimens containing a circular opening with different ratios of opening diameter to sample size λ (λ = 0.1, 0.13, 0.17, 0.2, and 0.23) were investigated under uniaxial compression with AE monitoring. The results indicate that the opening size strongly affected the peak strength and the elastic modulus. Crack initiation first started from the upper surface of the specimens, not from the periphery of the openings. Tensile and shear cracks coexisted on the roof and floor of the specimens, whereas tensile cracks were dominant on the two sides. The fracture mode of samples with openings was partially affected by the relative size of the pillars and openings. The AE response curves (in terms of counts, cumulative energy, cumulative counts, and b-value) show that brittle failure was mainly a progressive process. Moreover, the AE information corresponded well with microcrack evolution in the samples and thus can be used to predict sample failure. [ABSTRACT FROM AUTHOR]

The advancement of mobile Internet and ubiquitous computing is facilitating various crowdsourcing services in which individuals or organizations obtain goods and services with less time and monetary cost. Recently, crowdphotographing, an emerging self-service mode over the mobile Internet, is to recruit several users to take the pictures via incentive mechanism. Mobile users can earn the money by executing their requested sensing tasks. Thus, how to make an efficient pricing strategy is becoming a challenge issue in crowdphotographing. To this end, this paper mainly investigates the rationality and optimization of task pricing for crowdphotographing. First, we analyze the correlation among tasks pricing, location of members, and tasks. Then, a multivariable linear regression model is adopted for determining the task pricing strategy. Further, an improved pricing model is devised by considering the package of several tasks that can be packaged in terms of their locations distribution. [ABSTRACT FROM AUTHOR]

This paper investigates event-triggered control for first-order multi-agent systems with structurally balanced signed graphs. A distributed continuous event-triggered consensus mechanism is firstly proposed for each agent, including the control law based on local sampled states, and the triggering condition constructed with its own state, the sampled states of itself and its neighbours, and an extra signal. No knowledge of the global network is needed. By applying Lyapunov stability theory and the algebraic graph theory, we prove that all agents can reach agreement with identical magnitude but opposite sign while Zeno behaviour is excluded. Moreover, the periodic implementation of the event-triggered consensus algorithm is proposed. The criteria of co-designing the triggering condition parameter and the verification period is established to guarantee the bipartite consensus of the systems. Finally, simulations, including a numerical example and a group of multiple mobile robots, are shown to illustrate the efficiency of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

The last few decades have witnessed a steady increase in medicine prescriptions for the treatment of biometric markers rather than obvious physiological symptoms; especially, the over-the-counter (OTC) medicine experiences rated by patients have huge potential to assist people to make more appropriate decisions. The most existing researches focus on the rating prediction and recommendations in E-commerce field rather than healthcare or medical treatments. In addition, the spatial and temporal factors were not considered in their recommendation mechanisms. Toward this end, this paper propose an efficient OTC medicines recommendation strategy based on tensor decomposition. Considering the impact of regional differentiation, a third-order tensor including medicine, location, and rating is constructed. To inference the usage of a new OTC medicine in a certain location, high-order singular value decomposition is applied to the above tensor for obtaining the intelligent recommendation. In order to evaluate the effectiveness of the proposed approach, we compared the conventional collaborative filtering approach and tensor-based approach in terms of precision and recall. The experimental results demonstrate that our proposed approach is significant better than collaborative filtering approach. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy linguistic term set is defined in hesitant fuzzy linguistic decision frameworks in which all decision makers are agreed on the primary linguistic scale and its membership functions; however, this situation does not always happen, because words mean different things to different people or there are some linguistic term sets with different semantics for different people. Hence, normalization of linguistic information is necessary. Inspired by normalizing linguistic information based on 2-tuple linguistic model, in this paper, the concept of generalized hesitant fuzzy linguistic term set is proposed when decision makers are not agreed on the primary linguistic scale and its membership functions, which is an extension of hesitant fuzzy linguistic term set. Then, t-norms and t-conorms are utilized to define new operations on generalized hesitant fuzzy linguistic term sets and their properties are discussed. Based on these new operations, the likelihood-based comparison relation of generalized hesitant fuzzy linguistic term sets is presented, which is an extension of the likelihood-based comparison relation of hesitant fuzzy linguistic term sets. Accordingly, the generalized hesitant fuzzy linguistic weighted average operator, generalized hesitant fuzzy linguistic weighted geometric operator, generalized hesitant fuzzy linguistic ordered weighted average operator and generalized hesitant fuzzy linguistic ordered weighted geometric operator are provided to fuse generalized hesitant fuzzy linguistic term sets in linguistic decision making. A case study is used to illustrate the practicality of the method based on generalized hesitant fuzzy linguistic term sets and compare with Lee and Chen's method, Rodriguez's method and Wei's method. It seems that the method based on generalized hesitant fuzzy linguistic term sets is flexible and useful method for hesitant fuzzy linguistic decision making. [ABSTRACT FROM AUTHOR]

Compressed Sensing (CS), as a promising paradigm for acquiring signals, is playing an increasing important role in many real-world applications. One of the major components of CS is sparse signal recovery in which greedy algorithm is well-known for its speed and performance. Unfortunately, in many classic greedy algorithms, such as OMP and CoSaMP, the real sparsity is a key prior information, but it is blind. In another words, the true sparsity is not available for many practical applications. Due to this disadvantage, the performance of these algorithms are significantly reduced. In order to avoid too much dependence of classic greedy algorithms on the true sparsity, this paper proposed an efficient reconstruction greedy algorithm for practical Compressed Sensing, termed stepwise optimal sparsity pursuit (SOSP). Differs from the existing algorithms, the unique feature of SOSP algorithm is that the assumption of sparsity is needed instead of the true sparsity. Hence, the limitations of sparsity in practical application can be tackled. Based on an arbitrary initial sparsity satisfying certain conditions, the SOSP algorithm employs two variable step sizes to hunt for the optimal sparsity step by step by comparing the final reconstruction residues. Since the proposed SOSP algorithm preserves the ideas of original algorithms and innovates the prior information of sparsity, thus it is applicable to any effective algorithm requiring known sparsity. Extensive experiments are conducted in order to demonstrate that the SOSP algorithm offers a superior reconstruction performance in terms of discarding the true sparsity. [ABSTRACT FROM AUTHOR]

Water surface visualization is an important research topic in computer graphics. This paper presents a novel method of water surface simulation by Secondary Distorted Textures (SDT), which realistically simulates and visualizes the reflection and refraction of calm water in real-time using current consumer-level hardware. The proposed method renders water surface using two stages of texture maps. 1. The first texture map stores the 3D geometries’ perspective reflection with respect to 3D perspective view; 2. The second texture map stores the distortion results of the first reflection map with lighting effects. Perlin noise is used to generate random height map. Reflection and refraction are obtained and stored in the secondary distorted texture map with Fresnel effects for each frame. At rendering pass, the SDT is directly tiled on water surface. This paper also discussed the rendering of transparent geometry, which have view-dependent of lighting effects features. Experimental results demonstrated that our method can render realistic geometry nearby dynamic water surface at the frame rates of 70-100 FPS by NVIDIA Quadro k5000 graphic card. The existing texture mapping and bump mapping methods were compared for illustrating that our method produced high realistic water surface without aliasing reflection. [ABSTRACT FROM AUTHOR]

Mobile Internet and Cloud Computing technologies facilitate the rapid development of mobile cloud services. Particularly, mobile cloud services recommendation has become increasingly important as services become increasingly prevalent over the Internet. Existing recommendation mechanisms mainly focus on Quality of Services (QoS). However, the continuous emerging of many mobile cloud services leads to a server issue on the information overload for users. Hence, how to recommend the suitable mobile cloud services to users for addressing this issue is important. To this end, this paper incorporates the unique property of soft set and creatively investigated the soft set-based mobile cloud service recommendation mechanism and then devised the corresponding recommendation algorithm. [ABSTRACT FROM AUTHOR]

The booming and novel emerging promising technologies on ubiquitous computing, GPS positioning, are facilitating the development of location-based services (LBSs). Particularly, understanding the dynamic topological structures of mobile users in LBSs who visit the same physical locations has many meaningful applications including friend recommendation, location-sensitive items recommendation, and privacy management. In this paper, we proposed a novel m-triadic concept-based approach for uncovering the social evolution of location-focused online communities in LBSs. Firstly, an m-triadic concept-based location-focused online communities detection approach is presented. Further, the social evolution of the community is characterized by the time series triadic concepts in which the objectives contain the targeted users. [ABSTRACT FROM AUTHOR]