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

ERSVC: An Efficient Routing Scheme for Satellite Constellation Adapting Vector Composition

AbstractCompared with GEO and MEO satellites, LEO satellite constellation is able to provide low-latency, broadband communications which is difficult to be provided by the GEO or MEO satellites. However, one of the challenges in LEO constellation is the development of an efficient and specialized routing scheme. This paper takes transmission rate and data transmission time into consideration, and proposes ERSVC, an efficient routing scheme for satellite constellation adapting vector composition. ERSVC reduces routing table computation complexity, and saves restricted satellite resources. By adapting vector composition method, the amount of data flowing into satellite constellation is maximized while the data traffic is well controlled. Correlative and comprehensive simulation indicates that ERSVC is superior to existing schemes for LEO satellite constellation, especially in balancing data flow

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

Re-Expression of AKAP12 Inhibits Progression and Metastasis Potential of Colorectal Carcinoma In Vivo and In Vitro

Background: AKAP12/Gravin (A kinase anchor protein 12) is one of the A-kinase scaffold proteins and a potential tumor suppressor gene in human primary cancers. Our recent study demonstrated the highly recurrent loss of AKAP12 in colorectal cancer and AKAP12 reexpression inhibited proliferation and anchorage-independent growth in colorectal cancer cells, implicating AKAP12 in colorectal cancer pathogenesis. Methods: To evaluate the effect of this gene on the progression and metastasis of colorectal cancer, we examined the impact of overexpressing AKAP12 in the AKAP12-negative human colorectal cancer cell line LoVo, the single clone (LoVo-AKAP12) compared to mock-transfected cells (LoVo-CON). Results: pCMV6-AKAP12-mediated AKAP12 re-expression induced apoptosis (3 % to 12.7%, p,0.01), migration (89.667.5 cells to 31.064.1 cells, p,0.01) and invasion (82.765.2 cells to 24.763.3 cells, p,0.01) of LoVo cells in vitro compared to control cells. Nude mice injected with LoVo-AKAP12 cells had both significantly reduced tumor volume (p,0.01) and increased apoptosis compared to mice given AKAP12-CON. The quantitative human-specific Alu PCR analysis showed overexpression of AKAP12 suppressed the number of intravasated cells in vivo (p,0.01). Conclusion: These results demonstrate that AKAP12 may play an important role in tumor growth suppression and the survival of human colorectal cancer

A strategy to apply quantitative epistasis analysis on developmental traits

Abstract Background Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits. Methods In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement. Results Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions. Conclusions We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes