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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

引用

31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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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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DLICA: Deep Learning based novel Strategy for Intelligent Channel Adaption in Wireless SDN-IoT Environment 3

DLICA: Deep Learning based novel Strategy for Intelligent Ch...

引用

3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Zabeehullah Arif, Fahim Abbas, Yawar Ahmad, Shahbaz Waseem, Muhammad Computer Software Engineering Military College of Signals Rawalpindi Pakistan Computer Science University of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798350332391

software-Defined Network (SDN) is an emerging networking paradigm and provides the cost-effective scalability and flexibility required for an IoT environment. Here, wireless SDN is supposed to efficiently use limited network resources as well as provide smart traffic routing in order to deliver a bunch of IoT-generated data to the remote destination (Cloud). As, with the increasing number of IoT nodes and a huge amount of data generation, there is an immense load on SDN-IoT channels. This paper will mainly focus on appropriate channels assignment to each SDN-IoT enabled switch in order to prevent network congestion and improve network performance. Traditional fixed channel assignment algorithms in the SDN-IoT environments have failed because of large-scale and unpredictable dynamic traffic flows. In this paper, we have proposed a novel deep learning-based model called DLICA for intelligent channel assignment in wireless SDN-IoT. Moreover, a deep learning-based future traffic load prediction model has also been proposed to adaptively assign channels according to the traffic load prediction. Simulations show that proposed deep learning models achieve excellent accuracy while predicting traffic load and appropriate channel assignment. Apart from high accuracy, the proposed models also improve network performance significantly as compared to traditional channel assignment algorithms. © 2023 IEEE.

关键词： Cost effectiveness

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Combatting the TrickBot Threat: Analysis, Impact, and Defensive Strategies in Cybersecurity 17

Combatting the TrickBot Threat: Analysis, Impact, and Defens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Cao, Jintao Null, Allie Stewart, Marissa Bhunia, Suman Salman, Mohammad Miami University Department of Computer Science and Software Engineering OxfordOH United States University of Anbar College of Computer Anbar Iraq

ISBN: (纸本)9798350351637

The TrickBot Botnet, emerging in late 2016, has been a significant cybersecurity threat, leveraging sophisticated attack vectors such as phishing emails, network vulnerabilities, and secondary payloads. This paper provides a comprehensive analysis of TrickBot's evolution, attack methodologies, and widespread impact on individual users, businesses, and government entities. Despite some disruptions to its infrastructure, TrickBot has demonstrated remarkable resilience, continually adapting its tactics to evade detection and enhance its malicious capabilities. Our research offers detailed insights into the operational mechanisms of TrickBot, supported by extensive data on its propagation and impact. Furthermore, we evaluate various defense strategies, including advanced technical measures and human-centric approaches, to mitigate the ongoing threat posed by TrickBot and similar botnets. The findings underscore the critical need for continuous vigilance and innovation in cybersecurity practices to effectively counter such persistent threats. © 2024 IEEE.

关键词： Bot (Internet)

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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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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs 22

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lu, Jincheng Cai, Miao Ye, Baoliu Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798331509712

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs. © 2024 IEEE.

关键词： Parallel architectures

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Privacy-preserving scheme of cotton supply chain based on blockchain 22

Privacy-preserving scheme of cotton supply chain based on bl...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Linlin Li, Wenwen Huo, Jing Bi, Xuehua Zhao, Kai Xinjiang University College of Software Urumqi China Xinjiang University College of Computer Science and Technology Urumqi China Xinjiang Medical University College of Medical Engineering and Technology Urumqi China

ISBN: (纸本)9798331509712

With the popularization and development of artificial intelligence and intelligent agriculture, the supply chain has become an effective management method. However, the complexity and variety of data types and the large amount of data in the supply chain make its privacy and security a major challenge. Especially in the cotton supply chain, due to the large number of participants involved and the varying levels of data security protection of each participant, it is easy to leak the user's identity privacy and private data in the supply chain during data sharing. Therefore, this paper is dedicated to improving privacy security in the cotton supply chain. First, we define the data in the cotton supply chain and divide it into private data and shared data, which is conducive to the secure storage and sharing of data in the cotton supply chain. Second, we introduce blockchain and advanced cryptography techniques and propose a method of the pseudo-identity encryption based on SM9, which ensures the security of data in the supply chain while ensuring the privacy and security of the identities of the data owners and data receivers, and realizes the tracking of the identities of the malicious users who have uploaded false data. Finally, we adopt the method of dual-chain storage and introduce IPFS to reduce the storage burden of blockchain as much as possible and carry out security and experimental analyses of the proposed scheme to verify its feasibility. © 2024 IEEE.

关键词： Differential privacy

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An Unsupervised Leak Detection Method via Assembling Twin Attention-based Prediction Models for Natural Gas Gathering Pipelines

An Unsupervised Leak Detection Method via Assembling Twin At...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Hao Zuo, Zhonglin Zheng, Li Liang, Shan Lü, Qingguo College of Automation Chongqing University Chongqing China College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350368604

Accurately detecting leaks in natural gas gathering pipelines cannot only help enterprises and governments timely cope with the safety management problems, but also maintain the reliable operation of pipelines. The data-driven approaches for leak detection have become a preferred solution with the widely installation of sensors in pipeline network. However, these methods face challenges in achieving high identification accuracy due to the lack of labeled leak data and insufficient representation learning. To overcome this difficulty, an unsupervised leak detection method that leverages twin attention-based prediction models is proposed for these gathering pipelines in this article. First, attention-based point and sequence prediction approaches are designed by joint utilization of attention mechanism (AM) and the cascade of one-dimensional convolutional network (1D-CNN) and long short-term memory (LSTM) network, where the sequence one also employs the operation of sequence repetition and flipping for better prediction. Based on the designed attention-based point and sequence prediction approaches, an unsupervised twin attention-based prediction structure is then introduced to jointly establish the normal pipeline models. Specifically, the point prediction model is mainly used to learn the short-term dependency patterns whereas the sequence prediction model for the long-term ones from the multivariate time series. Next, a fusion strategy is presented to fuse the prediction errors generated from the twin attention-based models for the computing of overall leak scores as well as exploiting their complementary detection sensibility. To obtain clear status of pipelines without affected by abnormal points resulting from device anomalies or noises, the minimum covariance determinant (MCD) approach is adopted to attain the reliable leak scores of the fused errors. The experimental results on datasets derived from real-world gathering pipelines validate the effectiveness of

关键词： Long short-term memory

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Server-Aided Revocable Identity-Based Signature over Lattices 11

Server-Aided Revocable Identity-Based Signature over Lattice...

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11th International Conference on Information Technology: IoT and Smart City, ICIT 2023

作者： Wang, Bingbing Zhang, Yanhua College of Software Engineering Zhengzhou University of Light Industry China College of Computer and Communication Engineering Zhengzhou University of Light Industry China

ISBN: (纸本)9798400709043

Revocable Identity-Based Signatures (RIBS) incorporate revocation mechanisms into traditional identity-based signatures, allowing for the revocation of signing capabilities for users. Specifically, RIBS periodically assigns update keys to non-revoked users. When a user needs to be revoked, a trusted Key Generation Center (KGC) ceases to assign new update keys to that user, rendering the revoked user unable to sign any message effectively within future time periods. However, previous approaches have imposed significant burdens on users, requiring extensive computational efforts and continuous communication with the KGC. These limitations pose challenges for users with limited computing, communication, and storage capabilities. To alleviate these issues, this paper proposes a Server-Aided Revocable Identity-Based Signature (SR-IBS) scheme with signing key exposure resistance. In this scheme, almost all of the user's workload can be delegated to a third-party server, which manages user public keys and update keys. Additionally, users do not need to communicate with the KGC during the key update phase, and they only need to keep a fixed-sized key. Finally, we prove that the proposed scheme is strongly unforgeable against selective-ID and adaptive chosen-message attacks in the random oracle model. © 2023 ACM.

关键词： Authentication

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Velocity Field-Based Surveillance Video Frame Deletion Detection Using Siamese Network 27th

Velocity Field-Based Surveillance Video Frame Deletion Detec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Su, Yang Tan, ShunQuan Huang, Jiwu College of Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031783111

Surveillance videos play a crucial role in providing evidences. However, the deletion of even a few frames can significantly impact the interpretation of events, while the deletion can be performed easily using video editing softwares without leaving visual traces. This paper introduces a novel method to detect video frame deletion based on velocity field characteristics. The main idea is to convert long videos into a feature sequence, which appears as a sequence containing outlier values for videos with frame deletions, and as a sequence without outlier values for original videos. The proposed approach employs a siamese network to distinguish frame deletions between adjacent frames. A video can be transformed into a feature sequence through the siamese network, and the resulting sequence is fed into a binary classifier for final classification. Experimental results demonstrate the effectiveness of our proposed approach, even for longer videos (e.g., 2000 frames) with minimal frame deletions (e.g., 1 frame). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital forensics

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