volume-17-Issue 2 (2022)
Latest Articles
Some Results on Colored Network Contraction
JUSPN, volume-17, Issue 2 (2022) , PP 91 - 98
Published: 20 Dec 2022
DOI: 10.5383/JUSPN.17.02.006
by Flavio Lombardi, Elia Onofri from Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche (IAC-CNR), Rome (00185), Italy, Dipartimento di Matematica e Fisica, Roma Tre University, L. S. Murialdo, 1, Rome (00146), Italy and Member of the INdAM-GNCS research group
Abstract: Networks are pervasive in computer science and in real world applications. It is often useful to leverage distinctive node features to regroup such data in clusters, by making use of a single representative node per cluster. Such contracted graphs can help identify features of the original networks that were not visible before. As an example, we can identify contiguous nodes having the same discrete property in a social network. Contracting a graph allows a more scalable analysis of the interactions and structure of the network nodes. This paper delves into the problem of contracting possibly large colored networks into smaller and more easily manageable representatives. It also describes a simple but effective algorithm to perform this task. Extended performance plots are given for a range of graphs and results are detailed and discussed with the aim of providing useful use cases and application scenarios for the approach. read more... read less...
Keywords: Colored Networks, Graph Contraction, Greedy Algorithm, Graph Analysis
An Architecture for Cognitive Computing in Healthcare
JUSPN, volume-17, Issue 2 (2022) , PP 83 - 90
Published: 20 Dec 2022
DOI: 10.5383/JUSPN.17.02.005
by Ronald Tombe, F Mzee Awuor, Serestina Viriri from Kisii University, Kisii, Kenya and University of KwaZulu-Natal, Durban, South Africa
Abstract: The integrated impact of computing techniques and resources with big-data processing transforms human lifestyles by providing quality services ranging from healthcare to smart homes and effective interactions. However, many healthcare systems fail to consider patient emergencies and cannot provide a customized resource service. Cognitive computing is a requisite technology to create these intelligent systems based on artificial intelligence algorithms. This paper presents technologies for personalized healthcare services through cognitive computing. This paper investigates cognitive computing developments from discovering knowledge, cognitive science, and big-data analytics at the onset. Then, the system architecture for a cognitive computing system is given. Furthermore, this paper presents the technologies for cognitive computing healthcare improvement opportunities and their challenges. Finally, this paper discusses the representative intelligent systems of cognitive computing, including medical, robotic, and cognitive-communication systems. read more... read less...
Keywords: cognitive computing, big-data, healthcare, machine learning, pervasive computing, ubiquitous systems
Design and Specification of a Privacy-Preserving Registration for Blockchain-Based Energy Markets
JUSPN, volume-17, Issue 2 (2022) , PP 73 - 81
Published: 20 Dec 2022
DOI: 10.5383/JUSPN.17.02.004
by Michell Boerger, Philipp Lämmel, Nikolay Tcholtchev, and Manfred Hauswirth from Fraunhofer Institute for Open Communication Systems (FOKUS), Berlin, Germany and Technical University of Berlin, Berlin, Germany
Abstract: The challenges of climate change and the related demand to integrate non-plannable and weather-dependent renewable energy resources pose enormous challenges for the entire energy domain, e.g. in the context of grid control. These challenges reveal the need for new technical solutions and new business models while they indicate the required and inevitable transition to smart grids. Many blockchain-based solutions are being discussed in this context, ranging from peer-to-peer energy trading to grid-serving applications. However, especially in connection with public blockchains, clear security privacy challenges arise since the security and privacy of private data must be guaranteed while traceability must be avoided. Therefore, in this paper, we will specify privacy-protecting registration processes for blockchain-based flexibility markets that enable pseudonymous access to the latter. Furthermore, in collaboration with a governmental regulating institution named DGA, we will show that using an existing X.509-based PKI and RSA-based cryptographic processes, the integrity of all market participants can be guaranteed. This integrity is essential for the security-critical use of operating reserve. In addition, we will evaluate the specified processes in terms of efficiency, scalability, security, and privacy protection. read more... read less...
Keywords: Blockchain, privacy, security, encryption, distributed ledger, energy market
Low Rank Graph Regularization Embedding for 2D+3D Facial Expression Recognition
JUSPN, volume-17, Issue 2 (2022) , PP 67 - 72
Published: 14 Dec 2022
DOI: 10.5383/JUSPN.17.02.003
by Yunfang Fu, Yujuan Deng ,Yuekui Zhang, Zhengyan Yang, Ruili Qi from School of Computer Science & Engineering, Shijiazhuang University, Shijiazhuang, China, 050035 and Hebei Province Internet of Things Intelligent Perception and Application Technology Innovation Center,Shijiazhuang, China, 050035
Abstract: In this paper, a novel low rank graph regularization embedding for 3D facial expression recognition (LRGREFER) approach is proposed, in which the core tensor is utilized to characterize the low-rank attribute among the samples combined with the factor matrices with the graph regularization embedding. At first, a model based on a 4D tensor is constructed from the facial expression data. By Tucker decomposing the constructed 4D tensor, a resulting core tensor and factor matrices in different tensor modes are utilized to characterize the low-rankness among samples. Because of the loss of information during modelling the 4D tensor, the missing data from partly observed facial expression data are recovered by embedding the tensor completion. Finally, the proposed model is handled and solved by adopting the alternating direction method of multipliers (ADMM). Meanwhile, the classification prediction of facial expressions are implemented by Multi-class-SVM. Numerical experiments are conducted on BU-3DFE database. The experiment results have been verified that our proposed approach is more competitive. read more... read less...
Keywords: Low rank, Graph regularization embedding, Facial expression recognition, Tucker decomposition
An Optimized Kappa Architecture for IoT Data Management in Smart Farming
JUSPN, volume-17, Issue 2 (2022) , PP 59 - 65
Published: 07 Dec 2022
DOI: 10.5383/JUSPN.17.02.002
by Jean Bertin Nkamla Penka, Saïd Mahmoudi, Olivier Debauche from University of Mons, Faculty of Engineering - ILIA Lab / InforTech, Place du parc 20, Mons, Belgium, 7000, University of Liège - GxABT, Terra, Passage des Déportés 2, Gembloux, Belgium, 5030, University of Liège - GxABT, BioDynE - DEAL, Passage des Déportés 2, Gembloux, Belgium, 5030
Abstract: Agriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots, and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use case. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances read more... read less...
Keywords: Agriculture 4.0, IoT, Internet of Things, Kappa Architecture, Smart Farming, Smart Agriculture
New and Reliable Points Shifting - Based Algorithm for Indoor Location Services
JUSPN, volume-17, Issue 2 (2022) , PP 51 - 58
Published: 07 Dec 2022
DOI: 10.5383/JUSPN.17.02.001
by Tarek El Salti, Nelson Shaw, Joseph Chun-Chung Cheung, Edward R. Sykes from School of Applied Computing, Sheridan College, Oakville, Ontario, Canada, L6H 2L1 TELUS Communication Inc., 200 Consilium Place, Scarborough, Ontario, Canada, M1H 3E4 Centre for Mobile Innovation, Sheridan College, Oakville, Ontario, Canada, L6H 2L1
Abstract: Indoor localization is of great importance to several fields such as healthcare and asset tracking. However, many factors (e.g., multipath propagations) impact the quality of signals which are used to perform localizations. As a consequence, the precision and accuracy of the computed locations are heavily influenced. Therefore, the methodologies to compute indoor locations always need continuous refinements in terms of those metrics including the time complexity. For the last metric, It impacts the performance of mobile devices due to their limited resources. To address these challenges, a new set of fingerprinting algorithms was presented in this paper called Fingerprinting Line-Based Nearest Neighbour. This set shifts grid points potentially towards targets via a deterministic percentage. The running time of the set is upper bounded. Moreover, this paper presents the following: 1) an upper bound in terms of distance errors for the proposed algorithms, and 2) based on real experiments, the new algorithms (e.g., 90% shifting) improved the accuracy and precision, and had lower distance errors probabilities compared to those for the nearest neighbour-based algorithms (e.g., by 106% and 76%, respectively). read more... read less...
Keywords: : Indoor Location Services, Fingerprinting, Wi-Fi, Path Loss exponent, K-Nearest Neighbor