We present a choreographic framework for modelling and analysing concurrent probabilistic systems based on the PRISM model-checker. This is achieved through the development of a choreography language, which is a speci...
ISBN:
(纸本)9783031626968;9783031626975
We present a choreographic framework for modelling and analysing concurrent probabilistic systems based on the PRISM model-checker. This is achieved through the development of a choreography language, which is a specification language that allows to describe the desired interactions within a concurrent system from a global viewpoint. Employing choreographies provides a clear and comprehensive view of system interactions, enabling the discernment of process flow and detection of potential errors, thus ensuring accurate execution and enhancing system reliability. We equip our language with a probabilistic semantics and then define a formal encoding into the PRISM language and discuss its correctness. Properties of programs written in our choreographic language can be model-checked by the PRISM model-checker via their translation into the PRISM language. Finally, we implement a compiler for our language and demonstrate its practical applicability via examples drawn from the use cases featured in the PRISM website.
This special thematic session on accessible, smart, and integrated healthcare systems for elderly and persons with disabilities is targeted mainly towards presentation of publications around eHealth applications and s...
ISBN:
(纸本)9783031628481;9783031628498
This special thematic session on accessible, smart, and integrated healthcare systems for elderly and persons with disabilities is targeted mainly towards presentation of publications around eHealth applications and smart homes with focus on the topic of user involvement in all stages of product design, development and evaluation regarding accessibility and usability.
Blind and visually impaired individuals use tactile graphics to interpret any type of image, figure, or graph. However, the production of these materials is resource exhaustive-taking a lot of time, quality assurance,...
ISBN:
(纸本)9783031608834;9783031608841
Blind and visually impaired individuals use tactile graphics to interpret any type of image, figure, or graph. However, the production of these materials is resource exhaustive-taking a lot of time, quality assurance, and money. This research project uses recent advancements in electrotactile feedback to provide an accurate and timely approach to data visualizations of users who are blind or visually impaired. For this, we developed Electromouse, a mouse-based prototype where users can navigate the screen and feel an electrotactile sensation every time the cursor hits a significant line on the graphic presented on the screen. We performed an early exploration study with five blind adults to evaluate the effectiveness and safety of the prototype. Overall, participants were excited for this new method, but had suggestions for improvement related to the form factor, additional graphical information, and multimodal notification.
Generative AI has lately started being used in the software engineering process. Developers are relying on ChatGPT, GitHub Copilot or other tools to accelerate the development process. Previous works have provided an ...
ISBN:
(纸本)9783031664588;9783031664595
Generative AI has lately started being used in the software engineering process. Developers are relying on ChatGPT, GitHub Copilot or other tools to accelerate the development process. Previous works have provided an overview of the tools and have compared their capabilities. Nevertheless, the relation with software reuse in the framework of Generative AI has not been examined extensively. In this work, we are studying how generative AI techniques respond to and affect software reuse, with an emphasis on software licensing issues and end-users' data privacy. We are using the following five tools: OpenAI ChatGPT, Google Gemini, GitHub Copilot, TabNine and Amazon CodeWhisperer. We provide an overview of the tools and previous works that have used them when examining code generation, discuss the implications on software reuse, and use a simple front-end use case to showcase how they respond on licensing and end-users' data privacy issues. This work introduces also a conceptual model that can help in improvements in the discussed reuse aspects.
This article provides a roadmap for interdisciplinary design teams, outlining how to craft experience-oriented intervention strategies that enhance family's collaboration. It explores methods for pinpointing desig...
ISBN:
(纸本)9783031608803;9783031608810
This article provides a roadmap for interdisciplinary design teams, outlining how to craft experience-oriented intervention strategies that enhance family's collaboration. It explores methods for pinpointing design challenges within special needs populations and fosters an approach geared towards creating practical, real-world solutions that align with the perspectives of both designers and therapists. Highlighting the collaborative nature of the family, the article seeks to boost synergy across various fields of expertise, aiming to support the creation of effective and meaningful interventions for individuals with special needs.
In this research, we examine the rapid proliferation of ChatGPT, a leading-edge chatbot powered by sophisticated large language model (LLM) technology, and its privacy implications on societal perspectives. While it d...
ISBN:
(纸本)9783031612800;9783031612817
In this research, we examine the rapid proliferation of ChatGPT, a leading-edge chatbot powered by sophisticated large language model (LLM) technology, and its privacy implications on societal perspectives. While it demonstrates state-of-the-art capabilities in a variety of language-generating tasks, it also raises widespread public concerns regarding its societal impact. By employing advanced natural language processing (NLP) techniques, such as sentiment analysis and topic modeling, our study analyzes public attitudes towards ChatGPT using a dataset derived from Twitter. Our result shows that the overall sentiment is largely neutral and the public's heightened sensitivity to privacy and security breaches. Among a wide range of topics mentioned in tweets, the most popular topics are malicious phishing, data privacy, international policy and Employee data concern in workplace.
Deep-learning deformable image registration methods often struggle if test-image characteristic shifts from the training domain, such as the large variations in anatomy and contrast changes with different imaging prot...
ISBN:
(纸本)9783031456725;9783031456732
Deep-learning deformable image registration methods often struggle if test-image characteristic shifts from the training domain, such as the large variations in anatomy and contrast changes with different imaging protocols. Gradient descent-based instance optimization is often introduced to refine the solution of deep-learning methods, but the performance gain is minimal due to the high degree of freedom in the solution and the absence of robust initial deformation. In this paper, we propose a new instance optimization method, Neural Instance Optimization (NIO), to correct the bias in the deformation field caused by the distribution shifts for deep-learning methods. Our method naturally leverages the inductive bias of the convolutional neural network, the prior knowledge learned from the training domain and the multi-resolution optimization strategy to fully adapt a learning-based method to individual image pairs, avoiding registration failure during the inference phase. We evaluate our method with gold standard, human cortical and subcortical segmentation, and manually identified anatomical landmarks to contrast NIO's performance with conventional and deep-learning approaches. Our method compares favourably with both approaches and significantly improves the performance of deep-learning methods under distribution shifts with 1.5% to 3.0% and 2.3% to 6.2% gains in registration accuracy and robustness, respectively.
We present an innovative approach to the reactive synthesis of parity automaton specifications, which plays a pivotal role in the synthesis of linear temporal logic. We find that our method efficiently solves the SYNT...
ISBN:
(纸本)9783031572456;9783031572463
We present an innovative approach to the reactive synthesis of parity automaton specifications, which plays a pivotal role in the synthesis of linear temporal logic. We find that our method efficiently solves the SYNTCOMP synthesis competition benchmarks for parity automata from LTL specifications, solving all 288 models in under a minute. We therefore direct our attention to optimizing the circuit size and propose several methods to reduce the size of the constructed circuits: (1) leveraging different parity game solvers, (2) applying bisimulation minimisation to the winning strategy, (3) using alternative encodings from the strategy to an and-inverter graph, (4) integrating post-processing with the ABC tool. We implement these methods in the Knor tool, which has secured us multiple victories in the PGAME track of the SYNTCOMP competition.
Supply chain cyberattacks are on the rise as attackers are increasingly exploiting the intricate network of supplier connections between companies. Critical infrastructures too have been successfully targeted using th...
ISBN:
(纸本)9783031541285;9783031541292
Supply chain cyberattacks are on the rise as attackers are increasingly exploiting the intricate network of supplier connections between companies. Critical infrastructures too have been successfully targeted using this technique affecting their software and hardware estates, raising serious concerns due to the potential impact on public safety and the proper functioning of countries. This highlights the need to revise cybersecurity risk assessment strategies to stress the focus on threats originating from suppliers. This work proposes a novel supply chain cybersecurity risk assessment tailored for companies with limited cybersecurity expertise and constrained resources to execute risk assessment. Through a set of simple questions, this methodology first captures the perceived likelihood and impact of vulnerabilities and threats that derive from suppliers and target specific organisational assets and then generates cybersecurity risk scores for each relevant threat. A preliminary validation of the methodology is carried out, where generated risk scores are compared to evaluations provided by cybersecurity experts. The results show that the methodology produces risk scores that on average differ by 8% from those deriving from the experts' assessment, which corroborates the hypothesis that the methodology is reliable even though it does not require detailed information about the suppliers' cyber posture.
Few-shot image classification is a task that uses a small number of labeled samples to train a model to complete the classification task. Most few-shot image classification methods use small CNN-based models due to it...
ISBN:
(纸本)9789819985425;9789819985432
Few-shot image classification is a task that uses a small number of labeled samples to train a model to complete the classification task. Most few-shot image classification methods use small CNN-based models due to its good performance under supervised learning. However, small CNN-based models have performance bottlenecks under self-supervised learning with a large amount of unlabeled data. So we propose a model based on ViT for few-shot image classification. We propose a method combining Mask Image Modeling self-supervised learning and cross-architecture knowledge distillation to improve ViT. For fewshot image classification task, we propose a multi-perspective squeeze-excitation projector that is able to exploits the mutual information between samples in different perspectives, and aggregate in-class samples and discretize out-of-class samples. Finally, we construct a classifier based on it. Experimental results on Mini-ImageNet and TieredImageNet show that our model achieves an average of 2% improvement over the previous state-of-the-art.
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