Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

Research on strain anomalies and large earthquakes based on temporal and spatial crustal activities has been rapidly growing due to data availability, especially in Japan and Indonesia. However, many research works used local-scale case studies that focused on a specific earthquake characteristic using knowledgedriven techniques, such as crustal deformation analysis. In this study, a data-driven-based analysis is used to detect anomalies using displacement rates and deformation pattern features extracted from daily global navigation satellite system(GNSS) data using a machine learning algorithm. The GNSS data with188 and 1181 continuously operating reference stations from Indonesia and Japan, respectively, are used to identify the anomaly of recent major earthquakes in the last two decades. Feature displacement rates and deformation patterns are processed in several window times with 2560 experiment scenarios to produce the best detection using tree-based algorithms. Tree-based algorithms with a single estimator(decision tree), ensemble bagging(bagging, random forest and Extra Trees), and ensemble boosting(AdaBoost, gradient boosting, LGBM, and XGB) are applied in the study. The experiment test using realtime scenario GNSSdailydatareveals high F1-scores and accuracy for anomaly detection using slope windowing 365 and 730 days of 91-day displacement rates and then 7-day deformation pattern features in tree-based algorithms. The results show the potential for medium-term anomaly detection using GNSS data without the need for multiple vulnerability assessments.

Immunizations on small worlds of tree-based wireless sensor networks

The sensor virus is a serious threat,as an attacker can simply send a single packet to compromise the entire sensor network.Epidemics become drastic with link additions among sensors when the small world phenomena occur.Two immunization strategies,uniform immunization and temporary immunization,are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses.With the former strategy,the infection extends exponentially,although the immunization effectively reduces the contagion speed.With the latter strategy,recurrent contagion oscillations occur in the small world when the spatial-temporal dynamics of the epidemic are considered.The oscillations come from the small-world structure and the temporary immunization.Mathematical analyses on the small world of the Cayley tree are presented to reveal the epidemic dynamics with the two immunization strategies.

Classes of tree-based networks

Recently,so-called tree-based phylogenetic networks have attracted considerable attention.These networks can be constructed from a phylogenetic tree,called the base tree,by adding additional edges.The primary aim of this study is to provide sufficient criteria for tree-basedness by reducing phylogenetic networks to related graph structures.Even though it is generally known that determining whether a network is tree-based is an NP-complete problem,one of these criteria,namely edge-basedness,can be verified in linear time.Surprisingly,the class of edgebased networks is closely related to a well-known family of graphs,namely,the class of generalized series-parallel graphs,and we explore this relationship in full detail.Additionally,we introduce further classes of tree-based networks and analyze their relationships.

Code Clone Detection Method Based on the Combination of Tree-Based and Token-Based Methods

This article proposes the high-speed and high-accuracy code clone detection method based on the combination of tree-based and token-based methods. Existence of duplicated program codes, called code clone, is one of the main factors that reduces the quality and maintainability of software. If one code fragment contains faults (bugs) and they are copied and modified to other locations, it is necessary to correct all of them. But it is not easy to find all code clones in large and complex software. Much research efforts have been done for code clone detection. There are mainly two methods for code clone detection. One is token-based and the other is tree-based method. Token-based method is fast and requires less resources. However it cannot detect all kinds of code clones. Tree-based method can detect all kinds of code clones, but it is slow and requires much computing resources. In this paper combination of these two methods was proposed to improve the efficiency and accuracy of detecting code clones. Firstly some candidates of code clones will be extracted by token-based method that is fast and lightweight. Then selected candidates will be checked more precisely by using tree-based method that can find all kinds of code clones. The prototype system was developed. This system accepts source code and tokenizes it in the first step. Then token-based method is applied to this token sequence to find candidates of code clones. After extracting several candidates, selected source codes will be converted into abstract syntax tree (AST) for applying tree-based method. Some sample source codes were used to evaluate the proposed method. This evaluation proved the improvement of efficiency and precision of code clones detecting.

A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.

Tree-based backoff protocol for fast RFID tag identification

This paper proposes a tree-based backoff （TBB） protocol that reduces the number of iterations implemented in the procedure of tag collision arbitration in radio frequency identification （RFID） systems. This is achieved by employing the following mechanisms： one is send the request command iteratively to all tags in the interrogation zone until a single tag is identified. The other is backward to the parent node instead of root node to obtain the request parameters and send the request command again until all tags are identified. Compared with the traditional tree-based protocol, on average, simulated results show that the TBB protocol reduces the number of the iterations by 72.3% and the identification delay by 58.6% and achieves the goal of fast tag identification.

Tree-Based Index Overlay in Hybrid Peer-to-Peer Systems

Hybrid Peer-to-Peer （P2P） systems that construct overlay networks structured among superpeers have great potential in that they can give the benefits such as scalability, search speed and network traffic, taking advantages of superpeer-based and the structured P2P systems. In this article, we enhance keyword search in hybrid P2P systems by constructing a tree-based index overlay among directory nodes that maintain indices, according to the load and popularity of a keyword. The mathematical analysis shows that the keyword search based on semi-structured P2P overlay can improve the search performance, reducing the message traffic and maintenance costs.

Empirical evaluation of confidence and prediction intervals for spatial models of forest structure in Jalisco,Mexico

In recent years there has been an increasing interest in developing spatial statistical models for data sets that are seemingly spatially independent.This lack of spatial structure makes it difficult,if not impossible to use optimal predictors such as ordinary kriging for modeling the spatial variability in the data.In many instances,the data still contain a wealth of information that could be used to gain flexibility and precision in estimation.In this paper we propose using a combination of regression analysis to describe the large-scale spatial variability in a set of survey data and a tree-based stratification design to enhance the estimation process of the small-scale spatial variability.With this approach,sample units（i.e.,pixel of a satellite image） are classified with respect to predictions of error attributes into homogeneous classes,and the classes are then used as strata in the stratified analysis.Independent variables used as a basis of stratification included terrain data and satellite imagery.A decision rule was used to identify a tree size that minimized the error in estimating the variance of the mean response and prediction uncertainties at new spatial locations.This approach was applied to a set of n=937 forested plots from a state-wide inventory conducted in 2006 in the Mexican State of Jalisco.The final models accounted for 62% to 82% of the variability observed in canopy closure（%）,basal area（m2·ha-1）,cubic volumes（m3·ha-1） and biomass（t·ha-1） on the sample plots.The spatial models provided unbiased estimates and when averaged over all sample units in the population,estimates of forest structure were very close to those obtained using classical estimates based on the sampling strategy used in the state-wide inventory.The spatial models also provided unbiased estimates of model variances leading to confidence and prediction coverage rates close to the 0.95 nominal rate.

A generic framework for geotechnical subsurface modeling with machine learning

This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning(ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields(GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations(SOFs)estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods(extra trees(ETs), gradient boosting(GB), extreme gradient boosting(XGBoost), random forest(RF), general regression neural network(GRNN) and k-nearest neighbors(KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method(GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.

A Novel Approach towards Cost Effective Region-Based Group Key Agreement Protocol for Ad Hoc Networks Using Elliptic Curve Cryptography

This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, we propose a novel, secure, scalable and efficient region-based group key agreement protocol for ad hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using group elliptic curve diffie-hellman (GECDH) Protocol and links with other subgroups in a tree structure using tree-based group elliptic curve diffie-hellman (TGECDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group;hence computation load is distributed to many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs well for the key establishment problem in ad hoc network in terms of memory cost, computation cost and communication cost.

REMUDA: A Practical Topology Control and Data Forwarding Mechanism for Wireless Sensor Networks

In wireless sensor networks, topology control plays an important role for data forwarding efficiency in the data gathering applications. In this paper, we present a novel topology control and data forwarding mechanism called REMUDA, which is designed for a practical indoor parking lot management system. REMUDA forms a tree-based hierarchical network topology which brings as many nodes as possible to be leaf nodes and constructs a virtual cluster structure. Meanwhile, it takes the reliability, stability and path length into account in the tree construction process. Through an experiment in a network of 30 real sensor nodes, we evaluate the performance of REMUDA and compare it with LEPS which is also a practical routing protocol in TinyOS. Experiment results show that REMUDA can achieve better performance than LEPS.

Uptake and Use Efficiencies of Nutrients by Sesame and Bambara Nut Alley Crops as Influenced by Manuring in a Cashew-Based Intercropping System in the Guinea Savanna Agroecology of Nigeria

Field experiments were conducted at the experimental farm Cocoa Re-search Institute of Nigeria (CRIN) Sub-Station, Ochaja, in the Southern Guinea Savannaagro ecological zone of Nigeria to examine uptake and use efficien-cies of nutrients by Sesame and Bambara nut alley crops as influenced by manuring in a Cashew-based intercropping system. Experimental treatments were based on responses of sole and intercrop mixtures of Sesame and Bam-bara nut alley crops to Cocoa Pod Husk (CPH), pelletized organic fertilizer and NPK fertilizer in a cashew-based intercropping system. Data were collected on the growth and yield variables of the alley crops. Highest nitrogen harvest in-dex (NHI) for seed and leaf of alley crops were obtained from un-manure treated plants. Cocoa pod husk (CPH) significantly enhanced P uptake com-pared with other fertilizers applied. CPH improved Na, Ca, Mg Zn, Cu, P, K and carbohydrate in the leaves and Ca, Mg, Zn, Fe, Cu, crude fibre and car-bohydrate contents of seeds of sole crops while Sesame + Bambara had en-hanced contents of N, Ca, Mg, Zn, Cu, P, N, K, moisture, protein, and crude fi-bre, crude protein, moisture content in leaves. The effects of NPK were signifi-cant for N, K Ca, Zn, Fe, Cu, P, moisture and crude fibre, while in the un-manure (control) plots influenced N, fat and protein and nitrogen harvest index (NHI) of leaf and seeds. CPH and NPK fertilizers enhanced nutrient up-take and nitrogen harvest index of alley crops. Nutrient uptake was similar for the varieties of Sesame and Bambara nut as affected by the application of 4.84 and 9.68 Kg pelletized organic fertilizer. Sole Bambara had higher N and K concentration in leaves compared with Bambara +Sesame. In addition, sole Bambara had higher values of Physiology efficiency (PE), and fertilizer use ef-ficiency (FAE) compared to the mixed crops of Bambara + sesame. However, physiology efficiency (PE), and fertilizer use efficiency (FAE) were significantly lower for Bambara + Sesame. The un-manure plants had enhanced N, P and K uptake. Varietal effects were pronounced for most of the resource use effi-ciency variables measured. The alley crop varieties responded differently to 4.84 and 9.68 kg pelletized fertilizer treatments (Agronomy Efficiency (AE), N-removed at harvest and Internal Utilization Efficiency (IE) and partial fac-tor productivity (PFP)). Sesame variety NCRIBen04E had enhanced AE, N-remove at harvest, IE and PFP while variety E8 had significantly higher ap-parent Recovery Efficiency (RE), apparent Recovery Efficiency by difference (RE%), Physiology Efficiency (PE), Utilization Efficiency (UE), and internal Utilization Efficient (IE). Bambara variety TVSu999 had higher IUE, Agron-omy Efficiency (AE), Apparent Recovery Efficiency (RE), Physiology Effi-ciency (PE) and Fertilizer Agronomy using Efficiency respectively (FAE) com-pared to variety TVSu1166. The fertilizers affected most of the indicators of nutrient use efficiency (NUE) measured. The effects were significant on AE, agronomic N-use efficiency (ANUE), RE, UE and PFP. NPK fertilizer enhanced Physiology efficiency (PE) and Partial factor production. NPK fertilizer signifi-cantly enhanced NUE parameters compared to CPH and un-manure. CPH manure significantly influenced RE%, PE and IE. The Internal Utilization Effi-ciency and N-remove at harvest were compared with the un-manure plants (control). The effects of 9.68 kg/plot pelletized fertilizer, were pronounced on Agronomy Efficiency (AE), Apparent Recovery Efficiency by difference (RE%), Physiology Efficiency (PE), Utilization Efficiency (UE), N-removed at harvest and Internal Utilization Efficiency (IE). Similar trends were observed in the responses NUE of Sesame and Bambara manuring. The responses sole crops in terms of RE, PE UE PFP were similar while their intercrop combina-tions had significantly higher AE, RE, UE, PFP and N removed at harvest. Sole Sesame significantly influence Agronomy Efficiency (AE), Utilization Effi-ciency (UE), Internal Efficiency (IE) and Partial Fertilizer Production (PFP) and sole Bambara under NPK fertilizer had enhanced N-removed at harvest and apparent recovery by difference (RE%). Bambara + Sesame under cocoa pod husk (CPH) manure had enhanced apparent recovery efficiency by difference (RE%), fertilizer use efficiency (FAE) and internal utilization efficiency (IE). Sesame variety NCRIBen04E had enhanced Agronomy Efficiency (AE), N-removed at harvest and Internal Utilization Efficiency (IE) under 9.68 kg treatment while variety E8 had higher partial factor productivity (PFP) at 4.84 kg/plot pelletized organic fertilizer.

Thermal Energy Collection Forecasting Based on Soft Computing Techniques for Solar Heat Energy Utilization System

In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative energy source. However, thermal energy collection is influenced by solar radiation and weather conditions. In order to control a solar heat energy utilization system as accurate as possible, it requires method of solar radiation estimation. This paper proposes the forecast technique of a thermal energy collection of solar heat energy utilization system based on solar radiation forecasting at one-day-ahead 24-hour thermal energy collection by using three different NN models. The proposed technique with application of NN is trained by weather data based on tree-based model, and tested according to forecast day. Since tree-based-model classifies a meteorological data exactly, NN will train a solar radiation with smoothly. The validity of the proposed technique is confirmed by computer simulations by use of actual meteorological data.

A back analysis scheme for refined soil stratification based on integrating borehole and CPT data

Utilizing both borehole and Cone Penetration Testing(CPT)data in soil stratification helps to get more convincing soil stratification results.However,the soil classification results revealed by borehole(Unified Soil Classification System,USCS)and CPT tests(soil behavior type,SBT)are commonly not con-sistent.This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method.The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types.A novel boundary dic-tionary method is proposed to enhance the model performance on complex soil layer conditions.A prob-abilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data.The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees.The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization(SMBO)algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix.The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method.The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method.The results indicate that the proposed method is robust and effective.

High-dimensional indexing technologies for large scale content-based image retrieval: a review

The boom of Internet and multimedia technology leads to the explosion of multimedia information, especially image, which has created an urgent need of quickly retrieving similar and interested images from huge image collections. The content-based high-dimensional indexing mechanism holds the key to achieving this goal by efficiently organizing the content of images and storing them in computer memory. In the past decades, many important developments in high-dimensional image indexing technologies have occurred to cope with the 'curse of dimensionality'. The high-dimensional indexing mechanisms can mainly be divided into three categories: tree-based index, hashing-based index, and visual words based inverted index. In this paper we review the technologies with respect to these three categories of mechanisms, and make several recommendations for future research issues.

Updatable block-level deduplication of encrypted data with efficient auditing in cloud storage

Updatable block-level message-locked encryption(MLE) can efficiently update encrypted data, and public auditing can verify the integrity of cloud storage data by utilizing a third party auditor(TPA). However, there are seldom schemes supporting both updatable block-level deduplication and public auditing. In this paper, an updatable block-level deduplication scheme with efficient auditing is proposed based on a tree-based authenticated structure. In the proposed scheme, the cloud server(CS) can perform block-level deduplication, and the TPA achieves integrity auditing tasks. When a data block is updated, the ciphertext and auditing tags could be updated efficiently. The security analysis demonstrates that the proposed scheme can achieve privacy under chosen distribution attacks in secure deduplication and resist uncheatable chosen distribution attacks(UNC-CDA) in proof of ownership(PoW). Furthermore, the integrity auditing process is proven secure under adaptive chosen-message attacks. Compared with previous relevant schemes, the proposed scheme achieves better functionality and higher efficiency.

This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.