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Original scientific paperTo facilitate the automation process in the Internet of Things, the research issue of distinguishing prospective services out of many "similar" services, and identifying needed services w.r.t the criteria of Quality of Service (QoS), becomes very important. To address this aim, we propose heuristic optimization, as a robust and efficient approach for solving complex real world problems. Accordingly, this paper devises a cooperative evolution approach for service composition under the restrictions of QoS. A series of effective strategies are presented for this problem, which include an enhanced local best first strategy and a global best strategy that introduces perturbations. Simulation traces collected from real measurements are used for evaluating the proposed algorithms under different service composition scales that indicate that the proposed cooperative evolution approach conducts highly efficient search with stability and rapid convergence. The proposed algorithm also makes a well-designed trade-off between the population diversity and the selection pressure when the service compositions occur on a large scale.Key words: Cooperative evolution, IOT service composition, Quality of service Kooperativna evolucija za kvalitetno pružanje usluga u paradigmi Interneta stvari. Kako bi se automatizirali procesi u internetu stvati, nužno je rezlikovati bitne usluge u moru sličnih kao i identificirati potrebne usluge u pogledu kvalitete usluge (QoS). Kako bi doskočili ovome problemu prdlaže se heuristička optimizacija kao robustan i efikasan način rješavajne kompleksnih problema. Nadalje, učlanku je predložen postupak kooperativne evolucije za slaganje usluga uz ograničenja u pogledu kvalutete usluge. Predstavljen je niz efektivnih strategija za spomenuti problem uključujući strategije najboljeg prvog i najboljeg globalnog koje unose perturbacije u polazni problem. Simulacijski rezultati kao i stvarni podatci su korišteni u svrhu evaluacije prodloženog algoritma kako bi se osigurala efikasna pretraga uz stabilnost i brzu konvergenciju. Predloženi algoritam takoer vodi računa o odnosu izmeu različitosti populacije i selekcijskog pritiska kada je potrebno osigurati slaganje usluga na velikoj skali.Ključne riječi: kooperativna evolucija, Internet stvari pružanje usluge, kvaliteta usluge

Abstract-Bug triage is a process where bugs are assigned to developers. In large open source projects such as Mozilla and Eclipse, bug triage is time-consuming because numerous bugs are submitted everyday. To improve bug triage, many studies have proposed automatic approaches to recommend proper developers for resolving bugs. These approaches are based on machine learning algorithms, which treat bug triage like text classification. Although they are effective, the accuracy of them can be further improved. Our goal is to propose a method not only has good performance but also is simple. We propose a method based on relevant search technique to recommend developers for the given bugs. First, we construct an index for bugs to make them searchable. Then, for a given bug to be assigned, we utilize the index to search for the bugs related to it. Finally, we analyze these related bugs and recommend developers based on them. We conduct experiments on bugs of Mozilla and Eclipse to evaluate our method. The results indicate that our method has a good performance and outperforms machine learning algorithms like Naïve Bayes and SVM.

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneities in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.

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Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.

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A new ring‐fused streptovaricin analogue, named ansavaricin J, was unprecedently isolated from the culture of the genetically modified strains ΔstvP5 which derived from Streptomyces spectabilis CCTCC M2017417. Its structure was elucidated via comprehensive spectroscopic analyses, including 1D‐ and 2D‐NMR tests, and HR‐ESI‐MS data analysis. Notably, ansavaricin J and E represent the only two reported examples of heterocyclic ring‐fused streptovaricins thus far, however, it only showed insignificant antibacterial activities against Staphylococcus aureus.