Miracle-dz/UniFAR-FTUs huggingface dataset:
A Ship Recognition Method Based on Affinity Propagation

Accurate target classification is the keystone of the ship targets recognition in sea battlefields. Aiming at the deficiencies of supervised and unsupervised classified methods, we present a novel scheme called semi-supervised ship target recognition based on affinity propagation (AP). In order to circumvent the problem of choosing initial points, the method introduces affinity propagation clustering to construct classification model simply and effectively. Based on the idea of semi-supervised learning, a few restrictions of labeled flows and priori manifold distribution of sampled space are abstracted. Also, manifold similarity is defined. Hence, the semi-supervised method can not only largely reduce the complexity of marking sampled flows, but also nicely improve the performance of the classified. Theoretical analysis and experimental results show that that the proposed method is robust and can get better than KNN or SVM or HDR method. With the acquirement of high recognition rate of ship targets in the sea battlefields, undoubtedly, this approach is a feasible and efficient method.
Sensor network security issues at network layer

This paper presents an overview of the various vulnerabilities in WSN at network layer, and the defensive measures that can be taken against these threats. Resource limitations in the Wireless Sensor networks prevent us to directly apply the security mechanism of normal computer networks, a completely different set of security protocols exist for the sensor networks. Various security protocols for sensor networks have been evaluated against these vulnerabilities. Most of the attacks on some well known and widely used security protocols have been discussed, along with the possible countermeasures for these attacks. Some new emerging protocols have also been discussed in this context. Suitability of implementation of cryptographic techniques at the network layer has also been investigated for these severely resource constrained networks.
On Approximate Geometric K-clustering

For a partition of an n-point set X R d into k subsets (clusters) S 1 ; S 2 ; : : : ; S k , we consider the cost function P k i=1 P x2Si kx ? c(S i)k 2 , where c(S i) denotes the center of gravity of S i. For k = 2 and for any xed d and " > 0, we present a deterministic algorithm that nds a 2-clustering with cost no worse than (1 + ")-times the minimum cost in time O(n log n); the constant of proportionality depends polynomially on ". For an arbitrary xed k, we get an O(n log k n) algorithm for a xed ", again with a polynomial dependence on ".
Growth of Magnesium Oxide Thin Films Using Molecular Beam Epitaxy

Ultra thin MgO films (1-2 nm) are grown on Si/SiO 2 substrates using molecular beam epitaxy (MBE). Different growth conditions such as substrate temperatures, annealing conditions and growth rates are tested in order to achieve optimum parameters for the smooth and uniform growth. The films were characterized ex-situ using atomic force microscopy and x ray diffractio. Our results demonstrate MBE growth of MgO films with an rms roughness better than 0.5 nm on Si/SiO 2 substrates. These results are important for the applications of MgO films as tunnel barriers in spintronic devices.
Arginine Exposure Decreases Acidogenesis in Long-Term Oral Biofilm Microcosms

Arginine is used in dental health formulations to help prevent dental cavities. This study assessed the effects of the long-term dosing of laboratory dental plaques with an arginine dentifrice. Data indicate that the addition of arginine dentifrice during sucrose challenge significantly increased plaque pH, thus potentially mitigating cariogenesis. Several functional groups of bacteria associated with tooth decay were significantly decreased in the laboratory plaques during exposure to the arginine dentifrice. ABSTRACT Arginine is an important pH-elevating agent in the oral cavity. It has been incorporated in oral hygiene formulations to mitigate sensitivity and to prevent caries. In this investigation, the effects of sustained arginine dosing of dental plaque microcosms on bacteriological composition and pH were evaluated under controlled conditions. Plaque microcosms were established in constant-depth film fermentors (CDFFs) using salivary inocula and fed continuously with artificial saliva. To simulate resting and cariogenic states, the CDFFs were supplemented with sterile water or 5% sucrose, respectively. Plaques were then dosed twice daily with a dentifrice with 1.5% arginine arginine added (DA) or without arginine (DN). This regimen continued for over 3 weeks, after which fermentors were maintained without dosing. Microcosms were analyzed by differential viable counting, with a pH microelectrode, and by eubacterial DNA profiling. Sucrose dosing was associated with significantly (P < 0.001) decreased pH, significantly (P < 0.05) increased counts of total aerobes, Gram-negative anaerobes, aciduric species, acidogenic species, arginine utilizing species, bifidobacteria, lactobacilli and streptococci, and significant (P < 0.05) changes in DNA profiles. Plaques dosed with DA had a significantly (P < 0.001) higher pH than those dosed with DN, with or without sucrose supplementation. Dosing with DA but not DN significantly (P < 0.05) decreased counts of all functional bacterial groups apart from the total anaerobes in cariogenic plaques, and in resting plaques, dosing with DA significantly (P < 0.05) decreased counts of streptococci, lactobacilli, bifidobacteria, and acidogenic bacteria. In summary, sustained exposure of oral microcosms to arginine in formulation significantly increased plaque pH and significantly reduced the viability of cariogenic bacterial species. IMPORTANCE Arginine is used in dental health formulations to help prevent dental cavities. This study assessed the effects of the long-term dosing of laboratory dental plaques with an arginine dentifrice. Data indicate that the addition of arginine dentifrice during sucrose challenge significantly increased plaque pH, thus potentially mitigating cariogenesis. Counts of several functional groups of bacteria associated with tooth decay were significantly decreased in the laboratory plaques during exposure to the arginine dentifrice.
ZEB1 Promotes Invasion in Human Fetal Neural Stem Cells and Hypoxic Glioma Neurospheres

Diffuse spread through brain parenchyma and the presence of hypoxic foci rimmed by neoplastic cells are two cardinal features of glioblastoma, and low oxygen is thought to drive movement of malignant gliomas in the core of the lesions. Transcription factors associated with epithelial‐to‐mesenchymal transition (EMT) have been linked to this invasion, and we found that hypoxia increased in vitro invasion up to fourfold in glioblastoma neurosphere lines and induced the expression of ZEB1. Immunohistochemical assessment of 295 surgical specimens consisting of various types of pediatric and adult brain cancers showed that ZEB1 expression was significantly higher in infiltrative lesions than less invasive tumors such as pilocytic astrocytoma and ependymoma. ZEB1 protein was also present in human fetal periventricular stem and progenitor cells and ZEB1 inhibition impaired migration of in vitro propagated human neural stem cells. The induction of ZEB1 protein in hypoxic glioblastoma neurospheres could be partially blocked by the HIF1alpha inhibitor digoxin. Targeting ZEB1 blocked hypoxia‐augmented invasion of glioblastoma cells in addition to slowing them in normoxia. These data support the role for ZEB1 in invasive and high‐grade brain tumors and suggest its key role in promoting invasion in the hypoxic tumor core as well as in the periphery.
Automatic Asymmetric 3D-2D Face Recognition

3D Face recognition has been considered as a major solution to deal with unsolved issues of reliable 2D face recognition in recent years, i.e. lighting and pose variations. However, 3D techniques are currently limited by their high registration and computation cost. In this paper, an asymmetric 3D-2D face recognition method is presented, enrolling in textured 3D whilst performing automatic identification using only 2D facial images. The goal is to limit the use of 3D data to where it really helps to improve face recognition accuracy. The proposed approach contains two separate matching steps: Sparse Representation Classifier (SRC) is applied to 2D-2D matching, while Canonical Correlation Analysis (CCA) is exploited to learn the mapping between range LBP faces (3D) and texture LBP faces (2D). Both matching scores are combined for the final decision. Moreover, we propose a new preprocessing pipeline to enhance robustness to lighting and pose effects. The proposed method achieves better experimental results in the FRGC v2.0 dataset than 2D methods do, but avoiding the cost and inconvenience of data acquisition and computation of 3D approaches.
A machine learning based approach for table detection on the web

Table is a commonly used presentation scheme, especially for describing relational information. However, table understanding remains an open problem. In this paper, we consider the problem of table detection in web documents. Its potential applications include web mining, knowledge management, and web content summarization and delivery to narrow-bandwidth devices. We describe a machine learning based approach to classify each given table entity as either genuine or non-genuine. Various features reflecting the layout as well as content characteristics of tables are studied.In order to facilitate the training and evaluation of our table classifier, we designed a novel web document table ground truthing protocol and used it to build a large table ground truth database. The database consists of 1,393 HTML files collected from hundreds of different web sites and contains 11,477 leaf TABLE elements, out of which 1,740 are genuine tables. Experiments were conducted using the cross validation method and an F-measure of 95.89% was achieved.
Effects of Alternative Developer Decision-Making Models on the Production of Ecological Subdivision Designs: Experimental Results from an Agent-Based Model

Approaches to residential development have clear effects on the surrounding environments, including those on habitat protection, water quality, transportation and congestion costs, and loss of public open space. Ecological subdivision designs (ESDs) are a means to mitigate some of the most negative effects of low-density dispersed land-use patterns, yet there is not widespread adoption of these alternative approaches to subdivision development. In this paper we attempt to improve understanding of how developers make decisions over development designs and what influences those decisions. Using an agent-based model of residential-housing and land markets, the effects of different developer-decision frameworks on development designs and land use are assessed. The importance of uncertainty in the outcome of new designs, such as ESDs, and the effect of that uncertainty on the cost of credit are possible explanations for the prevalence of conventional, low-density development types, and may be impeding adoption of ESDs.
Design of an Office-Guide Robot for Social Interaction Studies

In this paper, the design of an office-guide robot for social interaction studies is presented. We are interested in studying the impact of passage behaviours in casual encounters. While the system offers assistance in locating the appropriate office that a visitor wants to reach, it is expected to engage in a passing behaviour to allow free passage for other persons that it may encounter. Through use of such an approach it is possible to study the effect of social interaction in a situation that is much more natural than out-of-context user studies. The system has been tested in an early evaluation phase when it worked for almost 7 hours. A total of 64 interactions with people were registered and 13 passage behaviors were performed to conclude that this framework can be successfully used for the evaluation of passing behaviors in natural contexts of operation
Interference Management for Distributed Parallel Applications in Consolidated Clusters

Consolidating multiple applications on a system can improve the overall resource utilization of data center systems. However, such consolidation can adversely affect the performance of some applications due to interference caused by resource contention. Despite many prior studies on the interference effects in single-node systems, the interference behaviors of distributed parallel applications have not been investigated thoroughly. With distributed applications, a local interference in a node can affect the whole execution of an application spanning many nodes. This paper studies an interference modeling methodology for distributed applications to predict their performance under interference effects in consolidated clusters. This study first characterizes the effects of interference for various distributed applications over different interference settings, and analyzes how diverse interference intensities on multiple nodes affect the overall performance. Based on the characterization, this study proposes a static profiling-based model for interference propagation and heterogeneity behaviors. In addition, this paper presents use case studies of the modeling method, two interference-aware placement techniques for consolidated virtual clusters, which attempt to maximize the overall throughput or to guarantee the quality-of-service.
Design and Implementation of a Monte Carlo Framework for Assessment of Spoiler Applications in Abutting Electron Fields

Background: Field matching problems in abutting electron fields can be managed by using spoilers. Objective: The aim of this study was to design a Monte Carlo framework for the assessment of spoiler application in abutting electron fields. Material and Methods: In this experimental study, a Siemens Primus treatment head was simulated for a 5 MeV electron beam using BEAMnrc, DOSXYZnrc and EGSnrc user codes. Validation of beam model was done by measurement using a MP3-M water tank and a Semi-flex Chamber-31010 (PTW, Freiburg, Germany). An in-house routine was developed to calculate the combined isodose curves resulting from simulated adjacent fields. The developed framework was analyzed using PMMA and chromium spoilers. Results: The penumbra width increased from 27.5 mm for open fields to 42 mm for PMMA and 40 mm for chromium. The maximum junction dose reduced from 115% for open fields to 107% for PMMA and 108% for chromium. R90 reduced about 6 mm for PMMA and 3 mm for chromium. Uniformity index reduced from 93% to 77% for both spoilers. Surface dose increased from 79% to 89% for PMMA and 88% for chromium. Conclusion: Using spoilers, penumbra width at the surface was increased, size and depth of hot spots as well as the therapeutic range were reduced and dose homogeneity at the junction of abutting electron fields was improved. For both spoilers, the uniformity index reduced, and surface percent dose increased. The results of this research can be used to optimize dose distribution in electron beam treatment using abutting fields.
Double Layered Learning for Biological Event Extraction from Text

This paper presents our approach (referred to as BioEvent) for protein-level complex event extraction, developed for the GENIA task (Kim et al., 2011b) of the BioNLP Shared Task 2011 (Kim et al., 2011a). We developed a double layered machine learning approach which utilizes a state-of-the-art minimized feature set for each of the event types. We improved the best performing system of BioNLP 2009 overall, and ranked first amongst 15 teams in finding "Localization" events in 2011. BioEvent is available at http://bioevent.sourceforge. net/
A Database Interface for Clustering in Large Spatial Databases

Both the number and the size of spatial databases are rapidly growing because of the large amount of data obtained from satellite images, X-ray crystallography or other scientific equipment. Therefore, automated knowledge discovery becomes more and more important in spatial databases. So far, most of the methods for knowledge discovery in databases (KDD) have been based on relational database systems. In this paper, we address the task of class identification in spatial databases using clustering techniques. We present an interface to the database management system (DBMS), which is crucial for the efficiency of KDD on large databases. This interface is based on a spatial access method, the R*-tree. It clusters the objects according to their spatial neighborhood and supports efficient processing of spatial queries. Furthermore, we propose a method for spatial data sampling as part of the focusing component, significantly reducing the number of objects to be clustered. Thus, we achieve a considerable speed-up for clustering in large databases. We have applied the proposed techniques to real data from a large protein database used for predicting protein-protein docking. A performance evaluation on this database indicates that clustering on large spatial databases can be performed both efficiently and effectively using our approach.
Machine learning algorithms in context of intrusion detection

Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel attacks. Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. Many machine learning techniques have been developed to cope with this problem. These machine learning algorithms develop a detection model in a training phase. This paper compares different supervised algorithms for the anomaly-based detection technique. The algorithms have been applied on the KDD99 dataset, which is the benchmark dataset used for anomaly-based detection technique. The result shows that not a single algorithm has a high detection rate for each class of KDD99 dataset. The performance measures used in this comparison are true positive rate, false positive rate, and precision.
A novel approach: CART algorithm for vertically partitioned database in multi-party environment

Advancement of technology and use of distributed database has made an increasing concern about privacy of private data. Collaboration and team work brings huge and demanding result. But organizations are unwilling to participate for data mining due to data leakage. For availing huge demanding result, it is required to use and collect data from different participating parties in a secured way, that is, no data leakage and to use correct decision tree algorithm that can correctly model the data and show accurate result. This paper represents how CART algorithm can be used for multi parties in vertically partitioned environment. For security purpose, secure sum and secure size of set intersection protocols are used.
A Model System for Studying the Transcriptomic and Physiological Changes Associated with Mammalian Host-Adaptation by Leptospira interrogans Serovar Copenhageni

Leptospirosis, an emerging zoonotic disease with worldwide distribution, is caused by spirochetes belonging to the genus Leptospira. More than 500,000 cases of severe leptospirosis are reported annually, with >10% of these being fatal. Leptospires can survive for weeks in suitably moist conditions before encountering a new host. Reservoir hosts, typically rodents, exhibit little to no signs of disease but shed large numbers of organisms in their urine. Transmission occurs when mucosal surfaces or abraded skin come into contact with infected urine or urine-contaminated water or soil. In humans, leptospires can cause a variety of clinical manifestations, ranging from asymptomatic or mild fever to severe icteric (Weil's) disease and pulmonary haemorrhage. Currently, little is known about how Leptospira persist within a reservoir host. Prior in vitro studies have suggested that leptospires alter their transcriptomic and proteomic profiles in response to environmental signals encountered during mammalian infection. However, no study has examined gene expression by leptospires within a mammalian host-adapted state. To obtain a more faithful representation of how leptospires respond to host-derived signals, we used RNA-Seq to compare the transcriptome of L. interrogans cultivated within dialysis membrane chambers (DMCs) implanted into the peritoneal cavities of rats with that of organisms grown in vitro. In addition to determining the relative expression levels of “core” housekeeping genes under both growth conditions, we identified 166 genes that are differentially-expressed by L. interrogans in vivo. Our analyses highlight physiological aspects of host adaptation by leptospires relating to heme uptake and utilization. We also identified 11 novel non-coding transcripts that are candidate small regulatory RNAs. The DMC model provides a facile system for studying the transcriptional and antigenic changes associated with mammalian host-adaption, selection of targets for mutagenesis, and the identification of previously unrecognized virulence determinants.
TOPTRAC: Topical Trajectory Pattern Mining

With the increasing use of GPS-enabled mobile phones, geo-tagging, which refers to adding GPS information to media such as micro-blogging messages or photos, has seen a surge in popularity recently. This enables us to not only browse information based on locations, but also discover patterns in the location-based behaviors of users. Many techniques have been developed to find the patterns of people's movements using GPS data, but latent topics in text messages posted with local contexts have not been utilized effectively. In this paper, we present a latent topic-based clustering algorithm to discover patterns in the trajectories of geo-tagged text messages. We propose a novel probabilistic model to capture the semantic regions where people post messages with a coherent topic as well as the patterns of movement between the semantic regions. Based on the model, we develop an efficient inference algorithm to calculate model parameters. By exploiting the estimated model, we next devise a clustering algorithm to find the significant movement patterns that appear frequently in data. Our experiments on real-life data sets show that the proposed algorithm finds diverse and interesting trajectory patterns and identifies the semantic regions in a finer granularity than the traditional geographical clustering methods.
Call by name, assignment, and the lambda calculus

We define an extension of the call-by-name lambda calculus with additional constructs and reduction rules that represent mutable variables and assignments. The extended calculus has neither a concept of an explicit store nor a concept of evaluation order; nevertheless, we show that programs in the calculus can be implemented using a single-threaded store. We also show that the new calculus has the Church-Rosser property and that it is a conservative extension of classical lambda calculus with respect to operational equivalence; that is, all algebraic laws of the functional subset are preserved.