Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Adding RLL Properties to Four CCSDS LDPC Codes Without Increasing Their Redundancy
Get PDFThis paper presents the construction of Run Length Limited (RLL) Error Control Codes (ECCs) from four Low Density Parity Check (LDPC) Codes specified by Consultative Committee for Space Data Systems (CCSDS). The obtained RLL-ECCs present a practical alternative to the CCSDS codes with pseudo-randomizers. Their advantage is that the maximal runlengths of equal symbols in their codeword sequences are guaranteed, which is not the case if the common approach with pseudo-randomizers is used. The other advantages are that no additional redundancy is introduced into encoded codewords and that the encoding and decoding procedures of the original error control CCSDS codes do not have to be modified in the following cases. In the first case if hard decoding is used and the transmission channel can be modeled as a Binary Symmetric Channel (BSC) or in the second case if soft decoding and coherent Binary Phase Shift Keying (BPSK) modulation is used and the appropriate transmission channel model is an Additive White Gaussian Noise (AWGN) channel
Semantic Similarity in a Taxonomy by Refining the Relatedness of Concept Intended Senses
Get PDFIn this paper, we present an evolution of a novel approach for evaluating semantic similarity in a taxonomy, based on the well-known notion of information content. Such an approach takes into account not only the generic sense of a concept but also its intended sense in a given context. In this work semantic similarity is evaluated according to a refined relatedness measure between the generic sense and the intended sense of a concept, leading to higher correlation values with human judgment with respect to the original proposal
Representation Learning Method of Graph Convolutional Network Based on Structure Enhancement
Get PDFNetwork representation learning has attracted widespread attention as a pre-processing process for some machine learning and deep learning tasks. However, most existing methods only consider influence of nodes' low-order neighbors to represent them. Either nodes' high-order neighbor or the intrinsic characteristic attributes of nodes are ignored, leading to the effect of network representation learning that needs to be improved. This paper proposes a novel model named Structure Enhanced Graph Convolutional Network (SEGCN) to address these limitations. SEGCN consists of the following components, i.e., the network structure enhancement to transform weak relationship into strong relationship, the node feature aggregation to fuse high-order neighbor information. Hence, the SEGCN model can simultaneously integrate network structure information, attribute information, and high-order neighbor relationships together. Experimental results for node classification and node clustering on six datasets show that SEGCN achieves better effectiveness and efficiency than state-of-the-art baselines
MGCN: Medical Relation Extraction Based on GCN
Get PDFWith the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks
Get PDFIn today's modern world, the usage of technology is unavoidable, and the rapid advances in the Internet and communication fields have resulted in the expansion of wireless sensor network (WSN) technology. However, WSN has been proven to be vulnerable to security breaches. The harsh and unattended deployment of these networks, combined with their constrained resources and the volume of data generated, introduces a major security concern. WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online stream analysis, allowing the identification of attacks and intrusions. Our aim is to develop an intelligent and efficient intrusion detection system by applying an important machine learning concept known as ensemble learning in order to improve detection performance. Although ensemble models have been proven to be useful in offline learning, they have received less attention in streaming applications. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis on a specialized WSN detection system (WSN-DS) dataset in order to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and Scheduling among normal network traffic. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84 % and 97.2 %, respectively. The above models are efficient and effective in dealing with concept drift while taking into account WSN resource constraints
Model for Spatiotemporal Crime Prediction with Improved Deep Learning
Get PDFCrime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this study proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100% outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better
Load Balancing for Resource Optimization in Internet of Things (IoT) Systems
Get PDFInternet of Things (IoT) has been recognised as a promising area for automating numerous processes, however, the major problem with IoT is its potential for rising complexities. Several approaches have moved attention to the edge nodes associated with IoT, hence concepts of edge-computing, resource allocation and load balancing are tantamount to a more robust heterogeneous IoT. The resource optimization terrain comes with several complications for the resource allocation and scheduling algorithms. Load balancing, one of the key strategies for improving system performance and resource utilization in distributed and parallel computing, generally views an effective load balancer as a 'traffic controller' of resources by directing tasks to available and capable resources. In this paper, a framework appropriate for modelling and reasoning about IoT resource optimization is developed. Further, implementation of an optimized resource allocation algorithm taking into consideration the users' quality of experience (QoE) and the quality of service (QoS) is made available. Simulation results authenticate analysis and validate the improved performance over existing work
PointHuman: Reconstructing Clothed Human from Point Cloud of Parametric Model
Get PDFIt is very difficult to accomplish the 3D reconstruction of the clothed human body from a single RGB image, because the 2D image lacks the representation information of the 3D human body, especially for the clothed human body. In order to solve this problem, we introduced a priority scheme of different body parts spatial information and proposed PointHuman network. PointHuman combines the spatial feature of the parametric model of the human body with the implicit functions without expressive restrictions. In PointHuman reconstruction framework, we use Point Transformer to extract the semantic spatial feature of the parametric model of the human body to regularize the implicit function of the neural network, which extends the generalization ability of the neural network to complex human poses and various styles of clothing. Moreover, considering the ambiguity of depth information, we estimate the depth of the parameterized model after point cloudization, and obtain an offset depth value. The offset depth value improves the consistency between the parameterized model and the neural implicit function, and accuracy of human reconstruction models. Finally, we optimize the restoration of the parametric model from a single image, and propose a depth perception method. This method further improves the estimation accuracy of the parametric model and finally improves the effectiveness of human reconstruction. Our method achieves competitive performance on the THuman dataset
Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation
Get PDFThis paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method
Retrieval Technology of Enterprise Data Center Resources Based on Density Peak Clustering Algorithm
Get PDFIn order to effectively ensure the retrieval effect of enterprise data center resources, improve the retrieval accuracy of enterprise data center resources, and shorten the retrieval time of enterprise data center resources, a retrieval technology of enterprise data center resources based on density peak clustering algorithm is proposed. Analytical clustering algorithms, density clustering algorithms, and density peak clustering algorithms are all types of clustering algorithms. To reduce the dimensionality of enterprise data center resources, the kernel principal component analysis method is used. The structure of the enterprise data center resource set is reorganized and the feature quantity of the enterprise data center resource distribution is extracted using feature space reorganization technology. On this basis, the density peak clustering is carried out on the data center resource set of enterprise, and the semantic association distribution model of data center resource retrieval in enterprise is constructed. Through the semantic registration and weighted vector combination control method, the retrieval of enterprise data center resources is realized. The experimental results show that the proposed algorithm has a good effect on the retrieval of enterprise data center resources, which can effectively improve the resource retrieval accuracy and shorten the resource retrieval time