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A series of environmental benign ionic liquid (IL) catalysts, synthesized by incorporating varied amounts of tungstophosphoric acid (TPA) and pyridinium propyl sulfobetaine (PPS) zwitterionic precursor, were exploited for homogeneous conversion of biomass. In particular, these water-soluble PPS-TPA IL ([PPSH] x H 3− x PW 12 O 40 ; x = 1.0–3.0) catalysts were evaluated for acetalization of benzaldehyde with glycol. The catalyst system revealed self-separation characteristics, which resulted in the formation of biphasic product/catalyst layers to render facile product separation and catalyst recycling. Among various PPS-TPA ILs examined, the [PPSH] 2 HPW 12 O 40 catalyst exhibited excellent durability and an optimal acetal yield over 85%, in good agreement with that predicted by factorial design of experiments and response surface methodology (RSM). The effects and correlations of different experimental variables such as reaction time, relative reactant concentration, amount of water-carrying agent, and amount of catalyst were addressed by the Box–Behnken design (BBD). The deduced optimal conditions lead to an acetal yield of 85.2%, which is consistent with experimental results and that predicted by the BBD model. The superior acetalization activities observed for the novel PPS-TPA IL catalysts are attributed to their highly acidic nature and weak mass transport resistance.

Hosting data query services in public clouds is an attractive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confidential range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloud-based computing, while enabling much faster query processing compared to the encryption-based approach. This demonstration will allow users to more intuitively understand the technical merits of the RASP approach via interactive exploration of the visual interface.

Running MapReduce programs in the cloud introduces this unique problem: how to optimize resource provisioning to minimize the monetary cost or job finish time for a specific job? We study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship among the time cost, the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs. Based on this cost function, we can solve a number of decision problems, such as the optimal amount of resources that can minimize monetary cost within a job finish deadline, minimize time cost under a certain monetary budget, or find the optimal tradeoffs between time and monetary costs. Experimental results show that the proposed approach performs well on a number of sample MapReduce programs in both the in-house cluster and Amazon EC2. We also conducted a variance analysis on different components of the MapReduce workflow to show the possible sources of modeling error. Our optimization results show that with the proposed approach we can save a significant amount of time and money, compared to randomly selected settings.

Lignin, a highly aromatic biopolymer extracted as a coproduct of wood pulping, was investigated as a suitable precursor for carbon fibers. Lignin was chemically modified and blended with poly(lactic acid) (PLA) biopolymer before melt spinning into lignin fibers. The chemical modification of raw lignin involved butyration to form ester functional groups in place of polar hydroxyl (–OH) groups, which enhanced the miscibility of lignin with PLA. Fine fibers were extracted and spooled continuously from lignin/PLA blends with an overall lignin concentration of 75 wt.%. The influence of chemical modification and physical blending of lignin with PLA on the resulting fiber was studied by analyzing the microstructure of the fibers using transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The influence of blend composition on the phase behavior was studied by differential scanning calorimetry (DSC). The effect of composition on the mechanical properties was studied by tensile tests of the lignin/PLA blend fibers. The thermal stability and carbon yield of the blended fibers with different concentrations of lignin were characterized by thermogravimetric analysis (TGA). The microstructure analysis of carbon fibers produced from lignin/PLA blends revealed composition dependent microporous structures inside the fine fibers.

Interactive visual cluster analysis is the most intuitive way for finding clustering patterns, validating algorithmic clustering results, understanding data clusters with domain knowledge, and refining cluster definitions. The most challenging step is visualizing multidimensional data and allowing user to interactively explore the data to identify clustering structures. In this paper, we systematically study the star-coordinate based visualization models and propose the optimized design that presents the best visualization results and supports the most efficient interaction methods. We explain the intuition behind the models and their link with random projection, and then optimize the visual design in terms of the efficiency of visual presentation and interactive operations. We also discuss the randomized visualization generation method, which can be used to generate batches of meaningful visualization results in parallel for big data. Finally, we present the experimental evaluation for the optimal design of models. This study is critical to generating effective visualization and minimizing the computational cost for visualizing data clusters for big data in the cloud.

The excessive accumulation of hypochlorite could cause tissue damage and a range of diseases. However, the exact role of hypochlorite in biological activity remains elusive. In many of the reported probes for hypochlorite, DMSO which is an efficient HOCl and •OH scavenger was used as solvent. Besides, no fluorescent probes have been reported to quantify the accumulation of HOCl in living cells and in vivo. In order to reach this purpose, in this study, we report two mitochondria-targeted probes (L1 and L2) for detecting ClO−. These probes which were easy to be synthesized, shown high sensitivity and fast response (within 3 min) to ClO−. Fluorescent imaging of probes in MCF-7 cells and in zebrafish indicated that L1/L2 could be used for detecting ClO− in living cells and organisms. ClO− could also be used as a dual input logic gate for L1 and L2. Probe L2 could be used to monitor oxidative stress-related biological processes in nude mice.

Results After review of the pre-specified IA, the IDMC recommended full analysis and disclosure of the results. With 256 IDFS events reported, administration of T-DM1 significantly improved IDFS compared with trastuzumab (unstratified HR = 0.50; 95% CI: 0.39 to 0.64; p

Background Ischemic stroke is significantly affected by the dysfunction of the miRNA network. Recent research has described that disordered expression of miR-130a is associated with ischemic stroke. Here, we aimed to investigate the possible mechanism of the miR-130a-mediated neuroprotection that follows ischemia-reperfusion (I/R) injury. Method This study was comprised of two models: oxygen-glucose deprivation/Reperfusion (OGDR) and middle cerebral artery occlusion (MCAO). RT-PCR and immunoblotting were used to examine gene expression levels, and MTT assay and flow cytometric analysis were used to examine cell states. We also used 2, 3, 5-triphenyltetrazolium chloride (TTC) staining to assess the cerebral infarct volume. Then, we employed bioinformatics analysis and luciferase reporter assay to identify and validate the target molecule of miR-130a, PTEN. Results Our findings indicated that miR-130a expression was lower in PC12 cells after OGDR (oxygen-glucose deprivation/reperfusion) and in rats after MCAO (middle cerebral artery occlusion). Moreover, ectopic-expression of miR-130a can significantly improve cell survival rate and reduce cell apoptosis and ROS production in PC12 cells after OGDR. In addition, re-expression of miR-130a yielded an obvious reduction in MCAO-induced infarct volume and neurological deficits in rats. Bioinformatics analysis revealed that PTEN was a miR-130a target and could overturn the effect of miR-130a on cerebral ischemia, both in vivo and in vitro. Therefore, we set out to further investigate the PTEN-affected PI3K/AKT pathway and found that upregulation of miR-130a activated the PI3K/AKT pathway. Conclusions Our data demonstrated that miR-130a prevented cerebral I/R damage by mediating the PTEN/PI3K/AKT axis. These preliminarily findings furthered our understanding of this mechanism and identified new potential therapeutic targets for ischemic stroke.

Large graph datasets have become invaluable assets for studying problems in business applications and scientific research. These datasets, collected and owned by data owners, may also contain privacy-sensitive information. When using public clouds for elastic processing, data owners have to protect both data ownership and privacy from curious cloud providers. We propose a cloud-centric framework that allows data owners to efficiently collect graph data from the distributed data contributors, and privately store and analyze graph data in the cloud. Data owners can conduct expensive operations in untrusted public clouds with privacy and scalability preserved. The major contributions of this work include two privacy-preserving approximate eigen decomposition algorithms (the secure Lanczos and Nystrom methods) for spectral analysis of large graph matrices, and a personalized privacy-preserving data submission method based on differential privacy that allows for the trade-off between data sparsity and privacy. For a N-node graph, the proposed approach allows a data owner to finish the core operations with only O(N) client-side costs in computation, storage, and communication. The expensive O(N2) operations are performed in the cloud with the proposed privacy-preserving algorithms. We prove that our approach can satisfactorily preserve data privacy against the untrusted cloud providers. We have conducted an extensive experimental study to investigate these algorithms in terms of the intrinsic relationships among costs, privacy, scalability, and result quality.

Collagen-Chitosan (COL-CS) porous scaffolds have been widely used as a dermal equivalent to induce fibroblasts infiltration and dermal regeneration. To improve the anti-bacterial properties, nano-TiO2 hydrosol was introduced into COL-CS scaffolds. TiO2/COL-CS porous scaffolds were fabricated through a freeze-drying process, and scanning electron microscopy (SEM) was employed to study the micro-structure of the scaffolds. Fourier transform infrared spectroscopy (FT-IR) was used to study the intermolecular interactions in the scaffolds. The swelling property, porosity, degradation, antibacterial behavior, red blood cell aggregation, and cytotoxicity of the composite were investigated. The results showed that the scaffold is good in permeability and it may provide a humid environment for wound repairing. The degradation in lysozyme solution for 4 weeks showed that porous scaffolds are good in stability, which may satisfy the wound coverage protection in the repairing period. An obvious inhibitory effect on Staphylococcus aureus of the porous scaffolds was found, and the red blood cells were easy to form clusters aggregation to stop bleeding. It was suggested that the TiO2/COL-CS composite scaffolds could be a promising candidate for wound repairing dressing.

Inappropriate tweets can cause severe damages on authors' reputation or privacy. However, many users do not realize the negative consequences until they publish these tweets. Published tweets have lasting effects that may not be eliminated by simple deletion because other users may have read them or third-party tweet analysis platforms have cached them. Regrettable tweets, i.e., tweets with identifiable regrettable contents, cause the most damage on their authors because other users can easily notice them. In this paper, we study how to identify the regrettable tweets published by \emph{normal individual users} via the contents and users' historical deletion patterns. We identify normal individual users based on their publishing, deleting, followers and friends statistics. We manually examine a set of randomly sampled deleted tweets from these users to identify regrettable tweets and understand the corresponding regrettable reasons. By applying content-based features and personalized history-based features, we develop classifiers that can effectively predict regrettable tweets.

Collaborative filtering plays an important role in online recommender systems, which provide personalized services to consumers by collecting and analyzing their rating histories. At the same time, such personalization may unfavorably incur privacy leakage, which has motivated the development of privacy-preserving collaborative filtering (PPCF) mechanisms. Most previous research efforts more or less impair the quality of recommendation. In this paper, we propose a social-aware algorithm called DynaEgo to improve the performance of PPCF. DynaEgo utilizes the principle of differential privacy as well as the social relationships to adaptively modify users’ rating histories to prevent exact user information from being leaked. Theoretical analysis is provided to validate our scheme. Experiments on a real data set also show that DynaEgo outperforms existent solutions in terms of both privacy protection and recommendation quality.

Analysis of big data has become an important problem for many business and scientific applications, among which clustering and visualizing clusters in big data raise some unique challenges. This demonstration presents the CloudVista prototype system to address the problems with big data caused by using existing data reduction approaches. It promotes a whole-big-data visualization approach that preserves the details of clustering structure. The prototype system has several merits. (1) Its visualization model is naturally parallel, which guarantees the scalability. (2) The visual frame structure minimizes the data transferred between the cloud and the client. (3) The RandGen algorithm is used to achieve a good balance between interactivity and batch processing. (4) This approach is also designed to minimize the financial cost of interactive exploration in the cloud. The demonstration will highlight the problems with existing approaches and show the advantages of the CloudVista approach. The viewers will have the chance to play with the CloudVista prototype system and compare the visualization results generated with different approaches.

Machine-learned ranking functions have shown successes in Web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a big bottleneck, that is, the problem of insufficient labeled training data, which has significantly slowed the development and deployment of machine-learned ranking functions for different domains. There are two possible approaches to address this problem: (1) combining labeled training data from similar domains with the small target-domain labeled data for training or (2) using pairwise preference data extracted from user clickthrough log for the target domain for training. In this article, we propose a new approach called tree-based ranking function adaptation (Trada) to effectively utilize these data sources for training cross-domain ranking functions. Tree adaptation assumes that ranking functions are trained with the Stochastic Gradient Boosting Trees method—a gradient boosting method on regression trees. It takes such a ranking function from one domain and tunes its tree-based structure with a small amount of training data from the target domain. The unique features include (1) automatic identification of the part of the model that needs adjustment for the new domain and (2) appropriate weighing of training examples considering both local and global distributions. Based on a novel pairwise loss function that we developed for pairwise learning, the basic tree adaptation algorithm is also extended (Pairwise Trada) to utilize the pairwise preference data from the target domain to further improve the effectiveness of adaptation. Experiments are performed on real datasets to show that tree adaptation can provide better-quality ranking functions for a new domain than other methods.

Background and Aim: Oxidative stress plays a critical role in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). However, there is still no large cohort study to explore the direct risk role of oxidative stress for NAFLD. This study is to test the hypothesis that elevated oxidative stress is a direct risk factor for the pathogenesis of NAFLD under controlling the potential effects of covariates. Methods: The levels of serum cholesterol, serum triglyceride, fasting plasma glucose and plasma reactive carbonyl species (RCS) were measured from 1204 Chinese Han adults, and the questionnaire and physical examination were administered to those with known and suspected risk factors for NAFLD. Results: Statistically significant high levels of blood pressure, fasting plasma glucose, serum cholesterol and triglyceride, body mass index, serum alanine aminotransferase and aspartate aminotransferase, and plasma RCS were observed in NAFLD subjects compared to healthy subjects (P

To study the role of oxidative stress in hypertension and pre-hypertension, this study analysed plasma levels of reactive carbonyl species (RCS) in 1204 Chinese Han adults. Results showed a statistically significant positive correlation (p0.001) between blood pressure and plasma RCS levels with or without being adjusted for covariates. Multivariate-adjusted odds ratio (OR) illustrated that, compared with the lowest quartile of plasma RCS levels, the highest quartile subjects had a 59% and a 130% increase in the risk for developing pre-hypertension and hypertension, respectively. The multi-interaction analysis manifested that the underlying mechanism of the increase of hypertensive risk or pre-hypertensive risk by overweight and unhealthy lifestyles might, at least in part, be through oxidative stress. In conclusion, these findings suggest that oxidative stress, as indicated by plasma RCS levels, are not the necessary consequence of pre-hypertension or hypertension, but reliable risk factors for developing pre-hypertension or hypertension in Chinese Han adults.

Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the Geometric Data Perturbation (GDP) method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well.

Isolation and analysis of rare circulating tumor cells (CTCs) is of great interest in cancer diagnosis, prognosis, and treatment efficacy evaluation. Acoustofluidic cell separation becomes an attractive method due to its contactless, noninvasive, simple, and versatile features. However, the indistinctive physical difference between CTCs and normal blood cells limits the purity of CTCs using current acoustic methods. Herein, we demonstrate a size-amplified acoustic separation and release of CTCs with removable microbeads. CTCs selectively bound to size-amplifiers (40 μm-diameter anti-EpCAM/gelatin-coated SiO2 microbeads) have significant physical differences (size and mechanics) compared to normal blood cells, resulting in an amplification of acoustic radiation force approximately a hundredfold over that of bare CTCs or normal blood cells. Therefore, CTCs can be efficiently sorted out with size-amplifiers in a traveling surface acoustic wave microfluidic device and released from size-amplifiers by enzymatic degradation for further purification or downstream analysis. We demonstrate a cell separation from blood samples with a total efficiency (E total) of ~ 77%, purity (P) of ~ 96%, and viability (V) of ~83% after releasing cells from size-amplifiers. Our method substantially improves the emerging application of rare cell purification for translational medicine.

Reactive carbonyl species (RCS) have been widely used as indicators of oxidative stress. However, the associations of carbonyl stress with aging process and biochemical alteration of erythrocyte are still poorly understood. Fresh blood samples in vacutainer tubes containing sodium heparinate were obtained from 874 volunteers who were divided into young, adult and old groups based on their age. Plasma RCS and thiols concentrations between different age groups and erythrocyte membrane protein carbonylation in the adult group were detected within 24 h of the blood sampling. Results showed that the plasma thiols concentration decreased gradually during aging process, and the p-values between all three groups are less than 0.05. The plasma RCS concentration in different age groups showed a nonlinear association with age. The levels in the young group were slightly higher than the adult group (not significant) and lower than the old group (p < 0.01). The protein carbonylation of erythrocyte membrane was positively correlated with plasma RCS concentration (p < 0.01), but not plasma thiols concentration. We conclude that higher levels of RCS, not lower levels of thiols, in plasma are a direct risk factor for the protein carbonylation of erythrocyte membrane. Owing to the decrease of thiols levels and increase of RCS levels during aging process, a shift from RCS-related redox allostasis to carbonyl stress would contribute to age-related biological dysfunction and even aging process.

In multiparty collaborative data mining, participants contribute their own data sets and hope to collaboratively mine a comprehensive model based on the pooled data set. How to efficiently mine a quality model without breaching each party's privacy is the major challenge. In this paper, we propose an approach based on geometric data perturbation and data mining service-oriented framework. The key problem of applying geometric data perturbation in multiparty collaborative mining is to securely unify multiple geometric perturbations that are preferred by different parties, respectively. We have developed three protocols for perturbation unification. Our approach has three unique features compared to the existing approaches: with geometric data perturbation, these protocols can work for many existing popular data mining algorithms, while most of other approaches are only designed for a particular mining algorithm; both the two major factors: data utility and privacy guarantee are well preserved, compared to other perturbation-based approaches; and two of the three proposed protocols also have great scalability in terms of the number of participants, while many existing cryptographic approaches consider only two or a few more participants. We also study different features of the three protocols and show the advantages of different protocols in experiments.

This paper presents SCALE, a fully automated transactional clustering framework. The SCALE design highlights three unique features. First, we introduce the concept of Weighted Coverage Density as a categorical similarity measure for efficient clustering of transactional datasets. The concept of weighted coverage density is intuitive and it allows the weight of each item in a cluster to be changed dynamically according to the occurrences of items. Second, we develop the weighted coverage density measure based clustering algorithm, a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Third, we introduce two clustering validation metrics and show that these domain specific clustering evaluation metrics are critical to capture the transactional semantics in clustering analysis. Our SCALE framework combines the weighted coverage density measure for clustering over a sample dataset with self-configuring methods. These self-configuring methods can automatically tune the two important parameters of our clustering algorithms: (1) the candidates of the best number K of clusters; and (2) the application of two domain-specific cluster validity measures to find the best result from the set of clustering results. We have conducted extensive experimental evaluation using both synthetic and real datasets and our results show that the weighted coverage density approach powered by the SCALE framework can efficiently generate high quality clustering results in a fully automated manner.

Analyzing clustering structures in data streams can provide critical information for real-time decision making. Most research in this area has focused on clustering algorithms for numerical data streams, and very few have proposed to monitor the change of clustering structure. Most surprisingly, to our knowledge, no work has been proposed on monitoring clustering structure for categorical data streams. In this paper, we present a framework for detecting the change of primary clustering structure in categorical data streams, which is indicated by the change of the best number of clusters (Best K) in the data stream. The framework uses a Hierarchical Entropy Tree structure (HE-Tree) to capture the entropy characteristics of clusters in a data stream, and detects the change of Best K by combining our previously developed BKPlot method. The HE-Tree can efficiently summarize the entropy property of a categorical data stream and allow us to draw precise clustering information from the data stream for generating high-quality BKPlots. We also develop the time-decaying HE-Tree structure to make the monitoring more sensitive to recent changes of clustering structure. The experimental result shows that with the combination of the HE-Tree and the BKPlot method we are able to promptly and precisely detect the change of clustering structure in categorical data streams.

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity measure. Based on the above measure, an agglomerative hierarchical clustering algorithm is developed and the Merging Dissimilarity Indexes, which are generated in hierarchical cluster merging processes, are used to find the candidate optimal number Ks of clusters of transactional data. Our experimental results on both synthetic and real data show that the new method often effectively estimates the number of clusters of transactional data.

The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been developed, surprisingly, none has satisfactorily addressed the problem of best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure, traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper, we study the entropy property between the clustering results of categorical data with different K number of clusters, and propose the BKPlot method to address the three important cluster validation problems: (1) How can we determine whether there is significant clustering structure in a categorical dataset? (2) If there is significant clustering structure, what is the set of candidate “best Ks”? (3) If the dataset is large, how can we efficiently and reliably determine the best Ks?

Objective: A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. Design: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster. Measurements: Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as "important" those articles selected by the Surgical Oncology Society. Results: Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. Conclusions: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.

Euclidean embedding algorithms transform data defined in an arbitrary metric space to the Euclidean space, which is critical to many visualization techniques. At big-data scale, these algorithms need to be scalable to massive data-parallel infrastructures. Designing such scalable algorithms and understanding the factors affecting the algorithms are important research problems for visually analyzing big data. We propose a framework that extends the existing Euclidean embedding algorithms to scalable ones. Specifically, it decomposes an existing algorithm into naturally parallel components and non-parallelizable components. Then, data parallel implementations such as MapReduce and data reduction techniques are applied to the two categories of components, respectively. We show that this can be possibly done for a collection of embedding algorithms. Extensive experiments are conducted to understand the important factors in these scalable algorithms: scalability, time cost, and the effect of data reduction to result quality. The result on sample algorithms: Fast Map-MR and LMDS-MR shows that with the proposed approach the derived algorithms can preserve result quality well, while achieving desirable scalability.

Inappropriate tweets may cause severe damages on the authors' reputation or privacy. However, many users do not realize the potential damages when publishing such tweets. Published tweets have lasting effects that may not be completely eliminated by simple deletion, because other users may have read them or third-party tweet analysis platforms have cached them. In this paper, we study the problem of identifying regrettable tweets from normal individual users, with the ultimate goal of reducing the occurrences of regrettable tweets. We explore the contents of a set of tweets deleted by sample normal users to understand the regrettable tweets. With a set of features describing the identifiable reasons, we can develop classifiers to effectively distinguish such regrettable tweets from normal tweets.

Based on single factor experiments, a three level, four variable central composite designs were carried out to establish a quadratic regression model for the catalytic synthesis of isoamyl isovalerate over sulfonated organic heteropolyacid salts as a function of molar ratio of alcohol to acid, reaction time, the amount of water-carrying agent and the amount of catalyst. The optimum conditions were obtained as follows: n (isoamyl alcohol): n (isovaleric acid) = 1.1:1, amount of catalyst was 6.4% of acid, reaction time 2 h and water-carrying agent 10 mL. Under these conditions, the yield of isoamyl isovalerate reached 97.5%. The ionic liquid could be reused six times without noticeable drop in activity. Under optimum conditions, catalysts showed a superior catalytic efficiency and reusability due to better superacidity, lower molecular transport resistance and self-separation characteristics.

With continued advances in communication network technology and sensing technology, there is astounding growth in the amount of data produced and made available through cyberspace. Efficient and high-quality clustering of large datasets continues to be one of the most important problems in large-scale data analysis. A commonly used methodology for cluster analysis on large datasets is the three-phase framework of sampling/summarization, iterative cluster analysis, and disk-labeling. There are three known problems with this framework which demand effective solutions. The first problem is how to effectively define and validate irregularly shaped clusters, especially in large datasets. Automated algorithms and statistical methods are typically not effective in handling these particular clusters. The second problem is how to effectively label the entire data on disk (disk-labeling) without introducing additional errors, including the solutions for dealing with outliers, irregular clusters, and cluster boundary extension. The third obstacle is the lack of research about issues related to effectively integrating the three phases. In this article, we describe iVIBRATE---an interactive visualization-based three-phase framework for clustering large datasets. The two main components of iVIBRATE are its VISTA visual cluster-rendering subsystem which invites human interplay into the large-scale iterative clustering process through interactive visualization, and its adaptive ClusterMap labeling subsystem which offers visualization-guided disk-labeling solutions that are effective in dealing with outliers, irregular clusters, and cluster boundary extension. Another important contribution of iVIBRATE development is the identification of the special issues presented in integrating the two components and the sampling approach into a coherent framework, as well as the solutions for improving the reliability of the framework and for minimizing the amount of errors generated within the cluster analysis process. We study the effectiveness of the iVIBRATE framework through a walkthrough example dataset of a million records and we experimentally evaluate the iVIBRATE approach using both real-life and synthetic datasets. Our results show that iVIBRATE can efficiently involve the user in the clustering process and generate high-quality clustering results for large datasets.

This poster presents a preliminary study on the PerturBoost approach that aims to provide efficient and secure classifier learning in the cloud with both data and model privacy preserved.

Privacy has been a big concern for users of social network services (SNS). On recent criticism about privacy protection, most SNS now provide fine privacy controls, allowing users to set visibility levels for almost every profile item. However, this also creates a number of difficulties for users. First, SNS providers often set most items by default to the highest visibility to improve the utility of social network, which may conflict with users' intention. It is often formidable for a user to fine-tune tens of privacy settings towards the user desired settings. Second, tuning privacy settings involves an intricate tradeoff between privacy and utility. When you turn off the visibility of one item to protect your privacy, the social utility of that item is turned off as well. It is challenging for users to make a tradeoff between privacy and utility for each privacy setting. We propose a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities. It mines the privacy settings of a large number of users in a SNS, e.g., Facebook, to generate latent trait models for the level of privacy concern and the level of utility preference. A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference. We crawl a large number of Facebook accounts and derive the privacy settings with a novel method. These privacy setting data are used to validate and showcase the proposed approach.

Cyber-spies rely on technologies such as rootkits to maintain a stealthy control of the victim kernel. Current techniques can detect changes to kernel code (e.g., SecVisor) and data (e.g., SBCFI), but have difficulties with transient kernel control flow attacks that insert execution requests into interrupt or kernel work queues (K-queues) without changing kernel code or data. Two examples implemented using Linux tasklets illustrate the effectiveness of K-queue attacks: key logger and CPU cycle stealer. Possible defenses to protect the kernel against K-queue attacks are outlined.

Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers and let the providers maintain the range-query service. With outsourced services, the data owner can greatly reduce the cost in maintaining computing infrastructure and data-rich applications. However, the service provider, although honestly processing queries, may be curious about the hosted data and received queries. Most existing encryption based approaches require linear scan over the entire database, which is inappropriate for online data analytics on large databases. While a few encryption solutions are more focused on efficiency side, they are vulnerable to attackers equipped with certain prior knowledge. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. We use RASP to generate indexable auxiliary data that is resilient to prior knowledge enhanced attacks. Range queries are securely transformed to the encrypted data space and then efficiently processed with a two-stage processing algorithm. We thoroughly studied the potential attacks on the encrypted data and queries at three different levels of prior knowledge available to an attacker. Experimental results on synthetic and real datasets show that this encryption approach allows efficient processing of range queries with high resilience to attacks.

The problem of efficient and high-quality clustering of extreme scale datasets with complex clustering structures continues to be one of the most challenging data analysis problems. An innovate use of data cloud would provide unique opportunity to address this challenge. In this paper, we propose the Cloud-Vista framework to address (1) the problems caused by using sampling in the existing approaches and (2) the problems with the latency caused by cloud-side processing on interactive cluster visualization. The CloudVista framework aims to explore the entire large data stored in the cloud with the help of the data structure visual frame and the previously developed VISTA visualization model. The latency of processing large data is addressed by the RandGen algorithm that generates a series of related visual frames in the cloud without user's intervention, and a hierarchical exploration model supported by cloud-side subset processing. Experimental study shows this framework is effective and efficient for visually exploring clustering structures for extreme scale datasets stored in the cloud.