Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness af...
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Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.
In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limit...
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In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data(decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly,and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.
The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing a...
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Video portrait segmentation(VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, the simplicity of existing VPS datasets leads to a limitat...
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Video portrait segmentation(VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, the simplicity of existing VPS datasets leads to a limitation on extensive research of the task. In this work, we propose a new intricate large-scale multi-scene video portrait segmentation dataset MVPS consisting of 101 video clips in 7 scenario categories,in which 10843 sampled frames are finely annotated at the pixel level. The dataset has diverse scenes and complicated background environments, which is the most complex dataset in VPS to our best *** the observation of a large number of videos with portraits during dataset construction, we find that due to the joint structure of the human body, the motion of portraits is part-associated, which leads to the different parts being relatively independent in motion. That is, the motion of different parts of the portraits is imbalanced. Towards this imbalance, an intuitive and reasonable idea is that different motion states in portraits can be better exploited by decoupling the portraits into parts. To achieve this, we propose a part-decoupling network(PDNet) for VPS. Specifically, an inter-frame part-discriminated attention(IPDA)module is proposed which unsupervisedly segments portrait into parts and utilizes different attentiveness on discriminative features specified to each different part. In this way, appropriate attention can be imposed on portrait parts with imbalanced motion to extract part-discriminated correlations, so that the portraits can be segmented more accurately. Experimental results demonstrate that our method achieves leading performance with the comparison to state-of-the-art methods.
Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing huma...
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Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. B...
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A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitat...
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Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.
Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC sy...
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As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each appli...
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With the widely use of mobile consumer electronics devices, location-based services becomes more and more popular in our lives, e.g., mapping services and ride-hailing services. Most of location-based services rely on...
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With the widely use of mobile consumer electronics devices, location-based services becomes more and more popular in our lives, e.g., mapping services and ride-hailing services. Most of location-based services rely on the support of efficient and accurate route planning. However, existing route planning algorithms mainly aim to plan for a single query in dynamic road networks, while ignoring the internal flows caused by massive planned route themselves, i.e., many vehicles may take the same road segments and thus cause traffic congestion and increase the global travel time. Therefore, in this paper, we focus on massive route planning in dynamic road networks to avoid such traffic congestion caused by the internal traffic flows. We first formally define the massive route planning with minimizing the global travel time (MRP-GTT) problem. Then, we prove that the MRP-GTT problem is NP-hard. To effectively solve it, we first design a novel game theory based algorithm (GTA) to reduce the global travel time for massive route queries. Because of the low efficiency of the global gaming for all queries, we then devise a game theory with query clustering algorithm (GTA-QC) in the paper, which first clusters queries based on the source and destination locations of queries, so that only queries in the same cluster can participate in a game to improve gaming efficiency. Extensive experiments on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithms. IEEE
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