Recommending with limited number of trusted users in social networks

© 2018 IEEE. To estimate the reliability of an unknown node in social networks, existing works involve as many opinions from other nodes as possible. Though this makes it possible to approximate the real property of the unknown nodes, the computational complexity increases as the scale of social networks getting bigger and bigger. We therefore propose a novel method which involve only limited number of social relations to predict the trustworthiness of the unknown nodes. The proposed method involves four rating prediction mechanisms: FM use the recommendation given by the most reliable recommender with the shortest trust propagation distance from the active user as the predicted rating, FMW weights the recommendation in FM, FA uses the mean value of recommendations with the shortest trust propagation distance from the active user as the predicted rating, and FAW weights recommendations in FA. The simulation results show that the proposed method can greatly reduce the rating prediction calculation, while the rating prediction losses are reasonable

Numerical investigation on rules of fracture propagation during hydraulic fracturing in heterogeneous coal-rock mass

To investigate rules of fracture propagation during hydraulic fracturing in heterogeneous coal-rock mass, a new mathematical model for hydraulic fracturing with seepage-damage coupling and its numerical algorithm are proposed. The rules of coal-rock mass heterogeneity, confining pressure, beforehand hydraulic slotting, and non-symmetric pressure gradient on fracture propagation are investigated. Numerical results show the following: (1) Compared to homogeneous coal-rock mass, the fracture propagation pattern exhibits a more zig-zag characteristic and the fracture initiation pressure is reduced in heterogeneous coal-rock mass. (2) Fracture propagation during borehole fracturing is mainly controlled by confining pressure ratio, and the fracture would propagate along the path with least resistance in coal-rock mass. (3) During hydraulic fracturing with beforehand hydraulic slotting, fracture propagation pattern would become more complex with slotting length increasing; the propagation direction of fracture is primarily controlled by principal stress difference, the larger of principal stress difference, the more difficult of oriented fracturing. (4) Non-symmetric pressure gradient can reduce breakdown pressure and influence fracture propagation pattern, which provides a beneficial guide for oriented fracturing. The simulation results are consistent with the theoretical solutions and experimental observations, which is promising to guide field operation of hydraulic fracturing to improve coalbed methane extraction

Mehanizam pretraživanja preporučitelja za sustave sigurnih preporučitelja u Internetu stvari

Intelligent things are widely connected in Internet of Things (IoT) to enable ubiquitous service access. This may cause heavy service redundant. The trust-aware recommender system (TARS) is therefore proposed for IoT to help users finding reliable services. One fundamental requirement of TARS is to efficiently find as many recommenders as possible for the active users. To achieve this, existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism. In this paper, we propose an efficient searching mechanism, named S_Searching: based on the scale-freeness of trust networks, choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.Inteligentni objekti su naširoko povezani u Internet stvari kako bi se omogućio sveprisutni pristup uslugama. To može imati za posljedicu veliku redundanciju usluga. Stoga je za pronalaženje pouzdane usluge u radu predložen vjerodostojan sustav preporučitelja (VSP). Temeljni zahtjev VSP-a je učinkovito pretraživanje maksimalnog mogućeg broja preporu čtelja za aktivnog korisnika. Kako bi se to postiglo, postojeći pristupi VSP-a u potpunosti pretražuju sigurnu mrežu što ima za posljedicu velike računske zahtjeve. Iako je sigurna mreža mreža bez skale, eksperimentima je pokazano kako VSP ne može naći zadovoljavajući broj preporučitelja direktnom primjenom klasičnog algoritma pretraživanja. U ovom radu je predložen učinkovit algoritam pretraživanja, nazvan S_Searching: temeljen na sigurnim mrežama bez skale koji koristi čvorove globalno najvećeg stupnja za izgradnju Skeleton-a i pretražuje preporučitelja pomoću Skeleton-a. Iskorištavanjem nadre.enih izlaznih stupnjeva čvorova Skeleton-a S_Searching može s visokom učinkovitošću pronaći preporučitelje. Eksperimentalni rezultati pokazuju kako S_Searching može naći gotovo jednak broj preporučitelja koji bi se pronašli potpunom pretragom, što je mnogo više od onoga što se postiže primjenom klasičnog algoritma pretrage na mreži bez skale, uz znatno smanjenje računske kompleksnosti i zahtjeva

Classification with class noises through probabilistic sampling

© 2017 Accurately labeling training data plays a critical role in various supervised learning tasks. Now a wide range of algorithms have been developed to identify and remove mislabeled data as labeling in practical applications might be erroneous due to various reasons. In essence, these algorithms adopt the strategy of one-zero sampling (OSAM), wherein a sample will be selected and retained only if it is recognized as clean. There are two types of errors in OSAM: identifying a clean sample as mislabeled and discarding it, or identifying a mislabeled sample as clean and retaining it. These errors could lead to poor classification performance. To improve classification accuracy, this paper proposes a novel probabilistic sampling (PSAM) scheme. In PSAM, a cleaner sample has more chance to be selected. The degree of cleanliness is measured by the confidence on the label. To accurately estimate the confidence value, a probabilistic multiple voting idea is proposed which is able to assign a high confidence value to a clean sample and a low confidence value to a mislabeled sample. Finally, we demonstrate that PSAM could effectively improve the classification accuracy over existing OSAM methods

Airflow and insulation effects on simultaneous syngas and biochar production in a top-lit updraft biomass gasifier

The objective of this study was to understand the effect of airflow and insulation on syngas and biochar generations of rice hulls and woodchips in a top-lit updraft gasifier. Biochar yield decreased with increasing airflow. The highest biochar yields of 39% and 27% were achieved at 8 L/min airflow for rice hulls and woodchips, respectively. The mass fraction of syngas in the products increased with increasing airflow, which ranged from 88–89% for rice hulls and 93–94% for woodchips. The H2 composition in syngas also increased at higher airflow rates; it peaked at 4.2–4.4% for rice hulls and 5.7–6.6% (v/v) for woodchips, which was not affected by insulation. The carbon monoxide content in syngas ranged from approximately 12 to 15% (v/v) and was not affected by airflow or insulation. Average tar content in syngas decreased for both biomasses when airflow increased, but adding insulation resulted in significantly higher tar content in syngas. The biomass type also had significant effects on gasifier performance. Biochar yields from rice hulls were greater than that from woodchips at all airflow rates. The lowest tar contents in syngas were approximately 1.16 and 11.88 g/m3 for rice hulls and woodchips, respectively.The objective of this study was to understand the effect of airflow and insulation on syngas and biochar generations of rice hulls and woodchips in a top-lit updraft gasifier. Biochar yield decreased with increasing airflow. The highest biochar yields of 39% and 27% were achieved at 8 L/min airflow for rice hulls and woodchips, respectively. The mass fraction of syngas in the products increased with increasing airflow, which ranged from 88–89% for rice hulls and 93–94% for woodchips. The H2 composition in syngas also increased at higher airflow rates; it peaked at 4.2–4.4% for rice hulls and 5.7–6.6% (v/v) for woodchips, which was not affected by insulation. The carbon monoxide content in syngas ranged from approximately 12 to 15% (v/v) and was not affected by airflow or insulation. Average tar content in syngas decreased for both biomasses when airflow increased, but adding insulation resulted in significantly higher tar content in syngas. The biomass type also had significant effects on gasifier performance. Biochar yields from rice hulls were greater than that from woodchips at all airflow rates. The lowest tar contents in syngas were approximately 1.16 and 11.88 g/m3 for rice hulls and woodchips, respectively

Cost-sensitive elimination of mislabeled training data

© 2017 Elsevier Inc. Accurately labeling training data plays a critical role in various supervised learning tasks. Since labeling in practical applications might be erroneous due to various reasons, a wide range of algorithms have been developed to eliminate mislabeled data. These algorithms may make the following two types of errors: identifying a noise-free data as mislabeled, or identifying a mislabeled data as noise free. The effects of these errors may generate different costs, depending on the training datasets and applications. However, the cost variations are usually ignored thus existing works are not optimal regarding costs. In this work, the novel problem of cost-sensitive mislabeled data filtering is studied. By wrapping a cost-minimizing procedure, we propose the prototype cost-sensitive ensemble learning based mislabeled data filtering algorithm, named CSENF. Based on CSENF, we further propose two novel algorithms: the cost-sensitive repeated majority filtering algorithm CSRMF and cost-sensitive repeated consensus filtering algorithm CSRCF. Compared to CSENF, these two algorithms could estimate the mislabeling probability of each training data more confidently. Therefore, they produce less cost compared to CSENF and cost-blind mislabeling filters. Empirical and theoretical evaluations on a set of benchmark datasets illustrate the superior performance of the proposed methods

The Effect of Gasification Conditions on the Surface Properties of Biochar Produced in a Top-Lit Updraft Gasifier

The effect of airflow rate, biomass moisture content, particle size, and compactness on the surface properties of biochar produced in a top-lit updraft gasifier was investigated. Pine woodchips were studied as the feedstock. The carbonization airflow rates from 8 to 20 L/min were found to produce basic biochars (pH > 7.0) that contained basic functional groups. No acid functional groups were presented when the airflow increased. The surface charge of biochar at varying airflow rates showed that the cation exchange capacity increased with airflow. The increase in biomass moisture content from 10 to 14% caused decrease in the pH from 12 to 7.43, but the smallest or largest particle sizes resulted in low pH; therefore, the carboxylic functional groups increased. Similarly, the biomass compactness exhibited a negative correlation with the pH that reduced with increasing compactness level. Thus, the carboxylic acid functional groups of biochar increased from 0 to 0.016 mmol g−1, and the basic functional group decreased from 0.115 to 0.073 mmol g−1 when biomass compactness force increased from 0 to 3 kg. BET (Brunauer-Emmett-Teller) surface area of biochar was greater at higher airflow and smaller particle size, lower moisture content, and less compactness of the biomassThe effect of airflow rate, biomass moisture content, particle size, and compactness on the surface properties of biochar produced in a top-lit updraft gasifier was investigated. Pine woodchips were studied as the feedstock. The carbonization airflow rates from 8 to 20 L/min were found to produce basic biochars (pH > 7.0) that contained basic functional groups. No acid functional groups were presented when the airflow increased. The surface charge of biochar at varying airflow rates showed that the cation exchange capacity increased with airflow. The increase in biomass moisture content from 10 to 14% caused decrease in the pH from 12 to 7.43, but the smallest or largest particle sizes resulted in low pH; therefore, the carboxylic functional groups increased. Similarly, the biomass compactness exhibited a negative correlation with the pH that reduced with increasing compactness level. Thus, the carboxylic acid functional groups of biochar increased from 0 to 0.016 mmol g−1, and the basic functional group decreased from 0.115 to 0.073 mmol g−1 when biomass compactness force increased from 0 to 3 kg. BET (Brunauer-Emmett-Teller) surface area of biochar was greater at higher airflow and smaller particle size, lower moisture content, and less compactness of the biomas

Improving Complex Network Controllability via Link Prediction

© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Complex network is a network structure composed of a large number of nodes and complex relationships between these nodes. Using complex network can model many systems in real life. The individual in the system corresponds to the node in the network and the relationship between these individuals corresponds to the edge in the network. The controllability of complex networks is to study how to enable the network to arrive at the desired state from any initial state by external input signals. The external input signals transmit to the whole network through some nodes in the network, and these nodes are called driver node. For the study of controllability of complex network, it is mainly to judge whether the network is controllable or not and how to select the appropriate driver nodes at present. If a network has a high controllability, the network will be easy to control. However, complex networks are vulnerable and will cause declining of controllability. Therefore, we propose in this paper a link prediction-based method to make the network more robust to different modes of attacking. Through experiments we have validated the effectiveness of the proposed method

Characterization of biochar from rice hulls and wood chips produced in a top-lit updraft biomass gasifier

Citation: James, M., Yuan, W., Boyette, M. D., Wang, D., & Kumar, A. (2016). Characterization of biochar from rice hulls and wood chips produced in a top-lit updraft biomass gasifier. Transactions of the Asabe, 59(3), 749-756. doi:10.13031/trans.59.11631The objective of this study was to characterize biochar produced from rice hulls and wood chips in a top-lit updraft gasifier. Biochar from four airflows (8, 12, 16, or 20 L min-1) and two insulation conditions (not insulated or insulated with 88.9 mm of fiberglass on the external wall of the gasifier) were evaluated. Measurement of elemental composition, higher heating value (HHV), and BET surface area and proximate analyses of the biochar were carried out. It was found that the airflow rate and reactor insulation significantly influenced the chemical composition of the biochar depending on the biomass type. For instance, the carbon content of biochar from rice hulls decreased from 40.9% to 27.2% and the HHV decreased from 14.8 to 10.2 MJ kg-1 as the airflow increased from 8 to 20 L min-1 when the reactor was insulated. In contrast, the carbon content of biochar from wood chips increased from 82% to 86% and the HHV stayed stable at 32.0 to 33.2 MJ kg-1 at the same conditions. Despite these variations, the BET surface area of biochar from both biomass types increased with increased airflow and additional insulation. For example, rice hull biochar had a maximum BET surface area of 183 m2g-1 at 20 L min-1 airflow with insulation. The BET surface of biochar from wood chips peaked at 405 m2 gg-1 at the same conditions