Computer Science and Information Systems最新文献

{"title":"SBEO: Smart Building Evacuation Ontology","authors":"Qasim Khalid, Alberto Fernández, Marin Lujak, Arnaud Doniec","doi":"10.2298/csis220118046k","DOIUrl":"https://doi.org/10.2298/csis220118046k","url":null,"abstract":"Semantically rich depiction of the concepts for context-aware indoor routing brings appealing benefits for the safety of occupants of smart spaces in emergency evacuation. In this paper, we propose Smart Building Evacuation Ontology (SBEO3), a reusable ontology for indoor spaces, based on three different data models: user, building, and context. We provide a common representation of indoor routing and navigation, describe users? characteristics and preferences, grouping of individuals and their role in a specific context, hazards, and emergency evacuation. Among other characteristics, we consider abilities of individuals, safety and accessibility of spaces related to each person, intensity, impact, and severity of an emergency event or activity. SBEO is flexible and compatible with other ontologies of its domain, including SEAS, SSN/SOSA, SEMA4A, and empathi. We evaluate SBEO based on several metrics demonstrating that it addresses the information needs for the context-aware route recommendation system for emergency evacuation in indoor spaces. In the end, a simulation-based application example exploits SBEO using Context-Aware Emergency Evacuation Software (CAREE).","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"78 1","pages":"51-76"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84719257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The proposal of new Ethereum request for comments for supporting fractional ownership of non-fungible tokens","authors":"Miroslav Stefanović, D. Przulj, Darko Stefanović, S. Ristić, D. Capko","doi":"10.2298/csis230127038s","DOIUrl":"https://doi.org/10.2298/csis230127038s","url":null,"abstract":"During the last couple of years, non-fungible tokens became the most prominent implementation of blockchain technology apart from cryptocurrencies. This is mainly due to their recent association with digital art, but the application of non-fungible tokens has been in the focus of researchers since the appearance of Blockchain 2.0. It was usually tightly coupled with the research on possible applications of blockchain technology in some real-life applications, such as land administration, healthcare, or supply chain management. Since the initial release of the Ethereum blockchain in 2015, until 2022, more than 44 million smart contracts have been created, and out of those that are still active, more than 70% are based on some prominent templates. In the Ethereum blockchain, the creation of non fungible tokens is usually based on Ethereum Request for Comments 721. In this paper, the authors are proposing the creation of a new standard that would support 23 fractional ownership of non-fungible tokens. Fractional ownership is necessary so 24 non-fungible tokens and blockchain technology could be applied to an even wider 25 number of use cases. This paper also presents an example of a possible implementation of the newly proposed standard in the Solidity programming language.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"143 1","pages":"1133-1155"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75932415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting 5G service orchestration with formal verification","authors":"Peter Backeman, Ashalatha Kunnappilly, C. Seceleanu","doi":"10.2298/csis211001059b","DOIUrl":"https://doi.org/10.2298/csis211001059b","url":null,"abstract":"The 5G communication technology has the ability to create logical net works, called network slices, which are specifically carved to serve particular application domains. Due to the mix of different application criticality, it becomes crucial to verify if the applications? service level agreements are met. In this pa per, we propose a novel framework for modeling and verifying 5G orchestration, considering simultaneous access and admission of new requests to slices as well as virtual network function scheduling and routing. By combining modeling in user friendly UML, with UPPAAL model checking and satisfiability-modulo-theories based model finding, our framework supports both modeling and formal verification of service orchestration. We demonstrate our approach on a e-health case study showing how a user, with no knowledge of formal methods, can model a system in UML and verify that the application meets its requirements.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"18 1","pages":"329-357"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82185798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse halftoning based on sparse representation with boosted dictionary","authors":"Jun Yang, Zihao Liu, Li Chen, Ying Wu, Gang Ke","doi":"10.2298/csis220808022y","DOIUrl":"https://doi.org/10.2298/csis220808022y","url":null,"abstract":"Halftone image is widely used in printing and scanning equipment. It is significant for the halftone image to be preserved and processed. For the different resolution of the display devices, the processing and displaying of halftone image are faced with great challenges, such as Moore pattern and image blur ring. The inverse halftone technique is required to remove the halftone screen. In this paper, we propose an inverse halftone algorithm based on sparse representation with the dictionary learned by two steps: deconvolution and sparse optimization in the transform domain to remove the noise. The main contributions of this paper include three aspects: first, we analysis the denoising effects for different training sets and the dictionary; Then we propose the denoising algorithm through adaptively learning the dictionary, which iteratively remove the noise of the training set and improve the dictionary; Then the inverse halftone algorithm is proposed. Finally, we verify that the noise level in the error diffusion linear model is fixed, and the noise level is only related to the diffusion operator. Experimental results show that the proposed algorithm has better PSNR and visual performance than state-of-the-art methods. The codes and constructed models are available at https://github.com/juneryoung2022/IH-WNNM.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"2 1","pages":"813-829"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82275490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure cloud internet of vehicles based on blockchain and data transmission scheme of map/reduce","authors":"Hua-Yi Lin","doi":"10.2298/csis220921056l","DOIUrl":"https://doi.org/10.2298/csis220921056l","url":null,"abstract":"Over the past few years, because of the popularity of the Internet of vehicles and cloud computing, the exchange of group information between vehicles is no longer out of reach. Through WiFi/5G wireless communication protocol, vehicles can instantly deliver traffic conditions and accidents to the back end or group vehicles traveling together, which can reduce traffic congestion and accidents. In addition, vehicles transmit real-time road conditions to the cloud vehicle management center, which can also share real-time road conditions and improve the road efficiency for pedestrians and drivers. However, the transmission of information in an open environment raises the issue of personal information security. Most of the security mechanisms provided by the existing Internet of vehicles require centralized authentication servers, which increase the burden of certificate management and computing. Moreover, the road side unit as a decentralized authentication center may be open to hacking or modification, but due to personal privacy and security concerns, vehicle-to-vehicle is not willing to share information with each other. Therefore, this study is conducted through blockchain to ensure the security of vehicle-based information transmission. Moreover, the elliptic curve Diffie-Hellman (ECDH) key exchange protocol and a secure conference key mechanism with direct user confirmation combined with the back-end cloud platform Map/Reduce is proposed to ensure the identities of Mappers and Reducers that participate in the cloud operation, avoid malicious participants to modify the transmission information, so as to achieve secure Map/Reduce operations, and improves vehicle and passenger traffic safety.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"137-156"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87857815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide detection based on efficient residual channel attention mechanism network and faster R-CNN","authors":"Yabing Jin, Ou Ou, Shanwen Wang, Yijun Liu, Haoqing Niu, X. Leng","doi":"10.2298/csis220831003j","DOIUrl":"https://doi.org/10.2298/csis220831003j","url":null,"abstract":"Accurate landslide detection plays an important role in land planning, disaster prediction and disaster relief. At present, field investigation and exploration based on professional personnel is the most widely used landslide mapping and detection technology, but this method consumes a lot of manpower and material resources and is inefficient. With the development of artificial intelligence, landslide identification and target detection based on deep learning have attracted more and more attention due to their remarkable advantages over traditional technologies. It is a technical problem to identify landslides from satellite remote sensing images. Although there are some methods at present, there is still room for improvement in the target detection algorithm of landslides against the background of the diversity and complexity of landslides. In this paper, target detection algorithm models such as Faster R-CNN apply to landslide recognition and detection tasks, and various commonly used recognition and detection algorithm network structures are used as the basic models for landslide recognition. Efficient residual channel soft thresholding attention mechanism algorithm (ERCA) is proposed, which intends to reduce the background noise of images in complex environments by means of deep learning adaptive soft thresholding to improve the feature learning capability of deep learning target detection algorithms. ERCA is added to the backbone network of the target detection algorithm for basic feature extraction to enhance the feature extraction and expression capability of the network. During the experiment ERCA combined with ResNet50, ResNet101 and other backbone networks, the objective indicators of detection results such as AP50 (Average Precision at IOU=0.50), AP75 (Average Precision at IOU=0.75) and AP (Average Precision) were improved, and the AP values were all improved to about 4%, and the final detection results using ResNet101 combined with ERCA as the backbone network reached 76.4% AP value. ERCA and other advanced channel attention networks such as ECA (Efficient Channel Attention for Deep Convolutional Neural Networks) and SENet (Squeeze-and-Excitation Networks) are fused into the backbone network of the target detection algorithm and experimented on the landslide identification detection task, and the detection results are that the objective detection indexes AP50, AP75, AP, etc. are higher for ERCA compared with other channel attention, and the subjective detection image detection effect and feature map visualization display are also better. We released our code at: https://github.com/fluoritess/Efficient-residual-channel-attention-mechanism-network-and-Faster-R-CNN.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"893-910"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81281257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel feature fusion model based on non-subsampled shear-wave transform for retinal blood vessel segmentation","authors":"Lijuan Feng, Zhang Fan","doi":"10.2298/csis221130028z","DOIUrl":"https://doi.org/10.2298/csis221130028z","url":null,"abstract":"Background: Fundus image is a projection of the inner surface of the eye, which can be used to analyze and judge the distribution of blood vessels on the retina due to its different shape, bifurcation and elongation. Vascular trees are the most stable features in medical images and can be used for biometrics. Ophthalmologists can effectively screen and determine the ophthalmic conditions of diabetic retinopathy, glaucoma and microaneurysms by the morphology of blood vessels presented in the fundus images. Traditional unsupervised learning methods include matched filtering method, morphological processing method, deformation model method, etc. However, due to the great difference in the feature complexity of different fundus image morphology, the traditional methods are relatively simple in coding, poor in the extraction degree of vascular features, poor in segmentation effect, and unable to meet the needs of practical clinical assistance. Methods: In this paper, we propose a new feature fusion model based on non-subsampled shearwave transform for retinal blood vessel segmentation. The contrast between blood vessels and background is enhanced by pre-processing. The vascular contour features and detailed features are extracted under the multi-scale framework, and then the image is postprocessed. The fundus images are decomposed into low frequency sub-band and high frequency sub-band by non-subsampled shear-wave transform. The two feature images are fused by regional definition weighting and guided filtering respectively, and the vascular detection image is obtained by calculating the maximum value of the corresponding pixels at each scale. Finally, the Otsu method is used for segmentation. Results: The experimental results on DRIVE data set show that the proposed method can accurately segment the vascular contour while retaining a large number of small vascular branches with high accuracy. Conclusion: The proposed method has a high accuracy and can perform vascular segmentation well on the premise of ensuring sensitivity.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"170 1","pages":"1729-1748"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sternum age estimation with dual channel fusion CNN model","authors":"Fuat Türk, M. Kaya, Burak Akhan, Sümeyra Çayiröz, E. Ilgit","doi":"10.2298/csis220825054t","DOIUrl":"https://doi.org/10.2298/csis220825054t","url":null,"abstract":"Although age determination by radiographs of the hand and wrist before the age of 18 is an area where there is a lot of radiological knowledge and many studies are carried out, studies on age determination for adults are limited. Studies on adult age determination through sternum multidetector computed tomography (MDCT) images using artificial intelligence algorithms are much fewer. The reason for the very few studies on adult age determination is that most of the changes observed in the human skeleton with age are outside the limits of what can be perceived by the human eye. In this context, with the dual-channel Convolutional Neural Network (CNN) we developed, we were able to predict the age groups defined as 20-35, 35-50, 51-65, and over 65 with 73% accuracy over sternum MDCT images. Our study shows that fusion modeling with dual-channel convolutional neural networks and using more than one image from the same patient is more successful. Fusion models will make adult age determination, which is often a problem in forensic medicine, more accurate.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"55 1","pages":"215-228"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79142762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ownership protection system for partial areas on image data using ethereum blockchain","authors":"Natsuki Fujiwara, Shohei Yokoyama","doi":"10.2298/csis230320061f","DOIUrl":"https://doi.org/10.2298/csis230320061f","url":null,"abstract":"Our proposed method utilizes blockchain technology to safeguard the ownership of specific regions within image data. In our approach, diverse values could be assigned to each region based on its importance, and only users with ownership rights can access these designated regions. This ensures the protection of ownership rights for individuals in any given region of an image. Identified regions are individually encrypted using an XOR cipher, and a corresponding key image is generated for decryption, thereby preserving the privacy of the encrypted region. Non-fungible tokens (NFTs) are employed to protect the key image and manage the ownership of each object in the image data. The NFT for the key image is generated by the key holder (who possesses the entire image), and the ownership NFT is acquired by the user who needs access to the key NFT. Furthermore, the ownership NFT and the key NFT are verified for a match by the judgment function, and only upon successful validation, the NFT is displayed on the screen. This method enables different values to be assigned to various parts of an image, facilitating the transfer and sharing of ownership. Additionally, the original image?s owner can benefit financially based on the value of the image, thus enhancing the overall security of image data.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135401334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel multipath QUIC protocol with minimized flow complete time for internet content distribution","authors":"Fang-Yi Lin, Wu-Min Sung, Lin Hui, Chih-Lin Hu, Nien-Tzu Hsieh, Yung-Hui Chen","doi":"10.2298/csis230818078l","DOIUrl":"https://doi.org/10.2298/csis230818078l","url":null,"abstract":"The rapid growth of network services and applications has led to an exponential increase in data flows on the internet. Given the dynamic nature of data traffic in the realm of internet content distribution, traditional TCP/IP network systems often struggle to guarantee reliable network resource utilization and management. The recent advancement of the Quick UDP Internet Connect (QUIC) protocol equips media transfer applications with essential features, including structured flow controlled streams, quick connection establishment, and seamless network path migration. These features are vital for ensuring the efficiency and reliability of network performance and resource utilization, especially when network hosts transmit data flows over end-to-end paths between two endpoints. QUIC greatly improves media transfer performance by reducing both connection setup time and transmission latency. However, it is still constrained by the limitations of single-path bandwidth capacity and its variability. To address this inherent limitation, recent research has delved into the concept of multipath QUIC, which utilizes multiple network paths to transmit data flows concurrently. The benefits of multipath QUIC are twofold: it boosts the overall bandwidth capacity and mitigates flow congestion issues that might plague individual paths. However, many previous studies have depended on basic scheduling policies, like round-robin or shortest-time-first, to distribute data transmission across multiple paths. These policies often overlook the subtle characteristics of network paths, leading to increased link congestion and transmission costs. In this paper, we introduce a novel multipath QUIC strategy aimed at minimizing flow completion time while taking into account both path delay and packet loss rate. Experimental results demonstrate the superiority of our proposed method compared to standard QUIC, Lowest-RTT-First (LRF) QUIC, and Pluginized QUIC schemes. The relative performance underscores the efficacy of our design in achieving efficient and reliable data transfer in real-world scenarios using the Mininet simulator.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135445069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}