Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms...
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ISBN:
(数字)9798350317152
ISBN:
(纸本)9798350317169
Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.
This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario ...
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ISBN:
(数字)9798350377514
ISBN:
(纸本)9798350377521
This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario and enhance the state-of-the-art genetic algorithm for trigger generation. The algorithm is used to design a feature selection method that prioritises attributes less prone to exploitation by attackers. The solution was evaluated using a dataset from the Koodous platform. The results show that the proposed improvements to the trigger selection algorithm were effective, resulting in a 10 percentage point increase. Furthermore, the proposed feature selection method significantly reduced the vulnerability of the ML system by 30%, without affecting the quality of the malicious application detection task execution.
For better protection of distributedsystems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better se...
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Agentic AI, with its autonomous and proactive decision-making, has transformed smart environments. By integrating Generative AI (GenAI) and multi-agent systems, modern AI frameworks can dynamically adapt to user prefe...
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In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This techn...
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In present paper, a general CO2 emission estimation approach is developed by means of a large range of open data as predictions. Such open data are related to human activity regarding services and (socio-economic) con...
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Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built ...
Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built environment with thermal images can assist in identifying the energy-leaking and potentially damage-prone areas. Although in recent years different researches performed deep learning and computer vision based thermal anomaly detection in built environment, several issues related to conducting strategic non-intrusive indoor thermal inspection using temporal thermal images, are still unresolved in uncontrolled environment of residential buildings. In this work, we propose a scalable thermal image-based monitoring approach for building envelopes combining the visual knowledge of structural joint information among different building components and their corresponding temporal thermal status. We collected longitudinal thermal images from indoor scenes of different building components (e.g., door, window, wall) and employed a high-level spatio-temporal graph (st-graph) to represent the structural connection among different building components and their temporal self-changes. Our proposed novel unsupervised spatio-temporal clustering framework assigns the cluster label to nodes in st-graph, combining its structural (the self and neighboring component) and temporal features which achieves better performance in identifying thermal variation compared to other clustering based approaches. We demonstrate thermal variation across the spots which indicates the potential energy leakage areas inside the built environment. The cluster patterns obtained from our proposed model assist in understanding the thermal characteristics of various surfaces at certain conditions, such as sun reflection and airflow in the inside built environment.
Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select ...
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Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.
As the Industry 4.0 shifts towards the adoption of autonomous mobile robots (AMRs) in warehouses, decentralized decision-making has become a key design principle. Multi-robot task allocation (MRTA) is a problem that i...
As the Industry 4.0 shifts towards the adoption of autonomous mobile robots (AMRs) in warehouses, decentralized decision-making has become a key design principle. Multi-robot task allocation (MRTA) is a problem that involves assigning tasks to AMRs while optimizing the performance of the system. However, modeling decentralized MRTA applications for optimization without a central instance poses significant challenges due to the autonomy and flexibility of AMRs. In this paper, we propose a simulative approach to address the fleet sizing problem combined with decentralized MRTA applications for AMRs. Based on simulation data, models have been developed that predict key performance indicators (KPIs) for different warehouse layouts and requirements, using techniques from machine learning and mathematical optimization. The model represents KPIs such as constraint satisfaction and utilization rates in a decentralized MRTA scenario including a self-organizing material flow application. Based on this model, we introduce a fleet size selection mechanism. This research contributes to the field of Industry 4.0 by providing a generalizable simulative approach that is adaptable to flexible warehouse environments, allowing for the application of any MRTA algorithm. Moreover, this approach allows the integration of different KPIs, facilitating the adaptation of requirements.
Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the...
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