Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community’s attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks. Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition. Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor. Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further. We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset. For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively

Simplifying large-scale communication networks with weights and cycles

PhDA communication network is a complex network designed to transfer information from a source to a destination. One of the most important property in a communication network is the existence of alternative routes between a source and destination node. The robustness and resilience of a network are related to its path diversity (alternative routes). Describing all the components and interactions of a large communication network is not feasible. In this thesis we develop a new method, the deforestation algorithm, to simplify very large networks, and we called the simplified network the skeleton network. The method is general. It conserves the number of alternative paths between all the sources and destinations when doing the simplification and also it takes into consideration the properties of the nodes, and the links (capacity and direction). When simplifying very large networks, the skeleton networks can also be large, so it is desirable to split the skeleton network into different communities. In the thesis we introduce a community-detection method which works fast and efficient for the skeleton networks. Other property that can be easily extracted from the skeleton network is the cycle basis, which can suffice in describing the cycle structure of complex network. We have tested our algorithms on the Autonomous System (AS)l evel and Internet Protocol address (IPA)le vel of the Internet. And we also show that deforestation algorithm can be extended to take into consideration of traffic directions and traffic demand matrix when simplifying medium-scale networks. Commonly, the structure of large complex networks is characterised using statistical measures. These measures can give a good description of the network connectivity but they do not provide a practical way to explore the interaction between the dynamical process and network connectivity. The methods presented in this thesis are a first step to address this practical problem

Remove-Win: a Design Framework for Conflict-free Replicated Data Collections

Internet-scale distributed systems often replicate data within and across data centers to provide low latency and high availability despite node and network failures. Replicas are required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the issue of conflict resolution among concurrent updates, which is often challenging and error-prone. The Conflict-free Replicated Data Type (CRDT) framework provides a principled approach to address this challenge. This work focuses on a special type of CRDT, namely the Conflict-free Replicated Data Collection (CRDC), e.g. list and queue. The CRDC can have complex and compound data items, which are organized in structures of rich semantics. Complex CRDCs can greatly ease the development of upper-layer applications, but also makes the conflict resolution notoriously difficult. This explains why existing CRDC designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDCs. To address the challenges above, we propose the Remove-Win Design Framework. The remove-win strategy for conflict resolution is simple but powerful. The remove operation just wipes out the data item, no matter how complex the value is. The user of the CRDC only needs to specify conflict resolution for non-remove operations. This resolution is destructed to three basic cases and are left as open terms in the CRDC design skeleton. Stubs containing user-specified conflict resolution logics are plugged into the skeleton to obtain concrete CRDC designs. We demonstrate the effectiveness of our design framework via a case study of designing a conflict-free replicated priority queue. Performance measurements also show the efficiency of the design derived from our design framework.Comment: revised after submissio

Skeleton and fractal scaling in complex networks

We find that the fractal scaling in a class of scale-free networks originates from the underlying tree structure called skeleton, a special type of spanning tree based on the edge betweenness centrality. The fractal skeleton has the property of the critical branching tree. The original fractal networks are viewed as a fractal skeleton dressed with local shortcuts. An in-silico model with both the fractal scaling and the scale-invariance properties is also constructed. The framework of fractal networks is useful in understanding the utility and the redundancy in networked systems.Comment: 4 pages, 2 figures, final version published in PR

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

Disassortativity of random critical branching trees

Random critical branching trees (CBTs) are generated by the multiplicative branching process, where the branching number is determined stochastically, independent of the degree of their ancestor. Here we show analytically that despite this stochastic independence, there exists the degree-degree correlation (DDC) in the CBT and it is disassortative. Moreover, the skeletons of fractal networks, the maximum spanning trees formed by the edge betweenness centrality, behave similarly to the CBT in the DDC. This analytic solution and observation support the argument that the fractal scaling in complex networks originates from the disassortativity in the DDC.Comment: 3 pages, 2 figure