This examine affords an empirical method to seabed photo class through the software of categorized systems getting to know techniques. The studies leverage a dataset of categorized seabed photos to teach and examine d...
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This paper presents LightFusionRec, a novel lightweight cross-domain recommendation system that integrates DistilBERT for textual feature extraction and FastText for genre embedding. Important issues in recommendation...
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Log-based anomaly detection plays a crucial role in ensuring the stability of software. However, current approaches for log-based anomaly detection heavily depend on a vast amount of labeled his-torical data, which is...
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Due to ever-increasing global trade activities and enhanced facilitation in the tourism market, cross-country traveling has seen a massive boost. This has resulted in crowded airports, posing challenges for aviation s...
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ISBN:
(纸本)9798350370058;9798350370164
Due to ever-increasing global trade activities and enhanced facilitation in the tourism market, cross-country traveling has seen a massive boost. This has resulted in crowded airports, posing challenges for aviation staff to screen the threat items from passenger baggage. Manual screening of baggage is cumbersome, tiring, and error-prone given the long working hours of the staff combined with concealing strategies incorporated by smugglers to deceive the security system. This has enhanced the requirement of autonomous and robust screening systems at security checkpoints. Researchers have been working rigorously to develop computer vision-based threat screening systems using different techniques. Recently, transformer-based techniques have been utilized in different classification and localization problems. These algorithms are more effective than traditional machine learning and CNN-based approaches, reducing the errors posed by region-based approaches. In this research, a vision transformer-based cross-domain classification algorithm is introduced for screening baggage threats. The framework uses Vision Transformers architecture and is primarily trained on COMPASS-XP dataset where it outperforms all the previous classification algorithms with an accuracy of 98% and F1-score of 99%. Furthermore, the model showcases the capability of screening threat items from novel datasets by employing small subsets of the corresponding data. Consequently, it exhibits adaptability towards novel image types and is ideal for situations where data scarcity forms the reason for low accuracy.
One of the major research questions in large databases is how to efficiently sample a random subset of records. This sample can then be used to estimate query results and optimize query execution plans and other tasks...
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ISBN:
(纸本)9783031683084;9783031683091
One of the major research questions in large databases is how to efficiently sample a random subset of records. This sample can then be used to estimate query results and optimize query execution plans and other tasks. In order to have quick access to the data, the common practice is to create an index, which is often implemented by using B+Trees. Existing state-of-the-art algorithms for random sampling over B+Trees result in a significant performance overhead. This paper proposes novel approaches for efficient random sampling over B+Trees in very large databases. We analyze the algorithms' correctness and use extensive simulation study, which showcases their superior performance compared to previous works while not affecting the quality of the random sample.
This paper presents a novel Convolutional Based Temporal Attention (CBTA) module that improves the performance of temporal convolutional networks (TCN) in lipreading tasks without requiring any additional data. Our CB...
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The rapidly evolving field of autonomous driving systems (ADSs) is full of promise. However, in order to fulfil these promises, ADSs need to be safe in all circumstances. This paper introduces ISS-Scenario, an autonom...
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ISBN:
(纸本)9783031646256;9783031646263
The rapidly evolving field of autonomous driving systems (ADSs) is full of promise. However, in order to fulfil these promises, ADSs need to be safe in all circumstances. This paper introduces ISS-Scenario, an autonomous driving testing framework in the paradigm of scenario-based testing. ISS-Scenario is designed for batch testing, exploration of test cases (e.g., potentially dangerous scenarios), and performance evaluation of autonomous vehicles (AVs). ISS-Scenario includes a diverse simulation scenario library with parametrized design. Furthermore, ISS-Scenario integrates two testing methods within the framework: random sampling and optimized search by means of a genetic algorithm. Finally, ISS-Scenario provides an accident replay feature, saving a log file for each test case which allows developers to replay and dissect scenarios where the ADS showed problematic behavior.
software reuse is an important principle within a software development process model, that efficiently and effectively manages the different variations that may exist between products of the same family, centered on a...
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Correct behavior of flight software against its requirements is a prime concern spanning its design, implementation, and operation. The emergence of "New Space" presents new challenges associated with small-...
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ISBN:
(纸本)9798350376975;9798350376968
Correct behavior of flight software against its requirements is a prime concern spanning its design, implementation, and operation. The emergence of "New Space" presents new challenges associated with small-scale missions which often involve open software frameworks, are developed by diverse teams, and employ rapid development methodologies which may not enjoy the rigorous quality assurance that institutional missions do. Hence, there is an overarching need to incorporate contemporary engineering techniques and methods for checking requirement satisfaction for such flight software. To this end, this paper proposes GOPRIME, designed to integrate goal monitoring within F', a renowned software development framework developed by the Jet Propulsion Laboratory for embedded and spaceflight systems. GOPRIME consists of three phases: (i) a design phase, where annotations are used to align system-level objectives with architectural components;(ii) an implementation phase, where code generation tailored for the DSL of F' is used to seamlessly automate the integration of goal monitoring functionality, and (iii) the operational phase, where the runtime state of annotated components is monitored, enabling evaluation of satisfaction of the overall goal model. We assess the development and operation overheads of instrumenting runtime goal monitoring over a characteristic case of a miniaturized satellite application.
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