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4th International Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th International Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Spiegler, Pascal Rasoulian, Amirhossein Xiao, Yiming Department of Computer Science and Software Engineering Concordia University Montreal Canada NeuroRx Research Montreal Canada

ISBN: (纸本)9783031811005

Intracranial hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis to improve treatment outcomes and patient survival rates. Recent advancements in supervised deep learning have greatly improved the analysis of medical images, but often rely on extensive datasets with high-quality annotations, which are costly, time-consuming, and require medical expertise to prepare. To mitigate the need for large amounts of expert-prepared segmentation data, we have developed a novel weakly supervised ICH segmentation method that utilizes the YOLO object detection model and an uncertainty-rectified Segment Anything Model (SAM). In addition, we have proposed a novel point prompt generator for this model to further improve segmentation results with YOLO-predicted bounding box prompts. Our approach achieved a high accuracy of 0.933 and an AUC of 0.796 in ICH detection, along with a mean Dice score of 0.629 for ICH segmentation, outperforming existing weakly supervised and popular supervised (UNet and Swin-UNETR) approaches. Overall, the proposed method provides a robust and accurate alternative to the more commonly used supervised techniques for ICH quantification without requiring refined segmentation ground truths during model training. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Boosting Write Performance of KV Stores: An NVM -Enabled Storage Collaboration Approach 40

Boosting Write Performance of KV Stores: An NVM -Enabled Sto...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi He, Jiajian Sun, Kaoyi Dong, Yunhao Chen, Jiaxian Ma, Chenlin Zhou, Amelie Chi Mao, Rui College of Computer Science and Software Engineering Shenzhen University China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This 'ZigZag' shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes. © 2024 IEEE.

关键词： Flash memory

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Programming Education for Young People using the Falling-Puzzle Game, 'Puyo Puyo' 14

Programming Education for Young People using the Falling-Puz...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Saito, Daisuke Tian, Ruochen Washizaki, Hironori Fukazawa, Yoshiaki Waseda University Global Software Engineering Laboratory Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350399431

This study utilizes the game rules of a falling-puzzle game, developed as a consumer-oriented digital game, in programming education for young people. When digital games are used in programming education, they are often designed specifically for that purpose. In this study, we focused on the game rules of the consumer falling-object puzzle game, 'Puyo Puyo.' Learning impact was investigated on 23 Japanese elementary, junior high, and senior high school students. The results show that the concepts of branching and arraying in programming were taught effectively;thus, consumer digital game rules can be used as a case study for programming education. © 2023 IEEE.

关键词： E-learning

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LeaderKV: Improving Read Performance of KV Stores via Learned Index and Decoupled KV Table 40

LeaderKV: Improving Read Performance of KV Stores via Learne...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi Yuan, Jianan Wu, Shangyu Liu, Huan Chen, Jiaxian Ma, Chenlin Qin, Jianbin Shenzhen University College of Computer Science and Software Engineering China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

Log-structured merge-tree (LSM-tree) is a storage architecture widely used in key-value (KV) stores. To enhance the read efficiency of LSM-tree, recent works utilize the learned index to learn the mapping between keys and locations. However, in existing learned-index-aided KV stores, inefficient design of the learned index and disk access significantly impact the read performance. How to design a learned KV store to improve index efficiency and minimize disk access remains a critical problem. This paper presents LeaderKV, a read-optimized LSM-tree-based KV store. LeaderKV employs decoupled KV tables (DK-Table) and efficient learned indexes for data retrieval. DKTables are storage files in Leader Kvbecause they avoid reading irrelevant data in collaboration with learned indexes during queries. A learned index called Leader is proposed to accelerate data retrieval within DKTable. Leader is composed of precise models and approximate models. A redirect mechanism is designed to reduce the cost of mispredictions in Leader. We integrate DKTable and Leader into LeaderKV and demonstrate its effectiveness using a variety of datasets and workloads. Experimental results show that LeaderKV significantly improves the read performance compared to representative schemes. © 2024 IEEE.

关键词： Efficiency

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

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IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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An Ingenious IoT Based Crop Prediction System Using ML and EL

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computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

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Less Traces Are All It Takes: Efficient Side-Channel Analysis on AES

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2080-2092页

作者： Xiao, Zhiyuan Wang, Chen Shen, Jian Wu, Q. M. Jonathan He, Debiao Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology the Zhejiang Provincial Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China University of Windsor Department of Electrical and Computer Engineering WindsorON Canada Wuhan University State Key Laboratory of Software Engineering Computer School Wuhan430072 China

In cryptography, side-channel analysis (SCA) is a technique used to recover cryptographic keys by examining the physical leakages that occur during the operation of cryptographic devices. Recent advancements in Deep Learning (DL) have greatly enhanced the extraction of crucial information from intricate leakage patterns. A considerable amount of research is dedicated to studying the SubByte (SB) operations of the advanced encryption standard (AES). This is because the SB process, which generates numerous transitions between 0s and 1s during encryption, results in significant energy leakage. However, traditional analysis models primarily focus on the initial round of SB operations in AES, which are less effective on mobile terminals where it is difficult to collect enough signals. These models often neglect additional operations and subsequent rounds, thus providing limited insights from small datasets. Consequently, this limitation has a direct impact on the accuracy and efficiency of key recovery. Our study uses ρ-test analysis to show that significant leakage occurs not only during the S-box operation but also during the AddRoundKey (AR) phase of AES. To address these challenges, we propose a new SCA method that is optimized for small sample sizes. This method includes a new comprehensive round trace labeling algorithm, which simultaneously analyzes the SB and AR stages of each AES round. Additionally, we introduce the peak precise localization algorithm to accurately identify the points of energy leakage during each encryption round. Our experiments, conducted with power and Electromagnetic (EM) datasets from the STM32F303 microcontroller, demonstrate that our method can reliably recover keys with as few as 20 traces. These results highlight the enhanced capability of our method in handling the complexities of small sample datasets in cryptographic analysis. © 2024 IEEE.

关键词： Information leakage

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software development processes in ocean system modeling

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 47-69页

作者： Reiner Jung Sven Gundlach Wilhelm Hasselbring Software Engineering Group Department of Computer Science Kiel UniversityKielGermany

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical *** science is a multidisciplinary discipline developing scientific models and simulations as ocean sys-tem models that are an essential research *** software engineering and information systems research,modeling is also an essential *** particular,business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise,so that the current process may be analyzed,improved and *** this paper,we employ process modeling for analyzing sci-entific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean *** interviewed domain experts in semi-structured inter-views,analyzed the results via thematic analysis,and modeled the results via the Busi-ness Process Modeling Notation(BPMN).The processes modeled as a result describe an aspired state of software development in the domain,which are often not(yet)*** enables existing processes in simulation-based system engineering to be improved with the help of these process models.

关键词： Scientific modeling software development processes process modeling the-matic analysis

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