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Knocking on Admin's Door: Protecting Critical Web Applications with Deception 21st

Knocking on Admin's Door: Protecting Critical Web Applicatio...

引用

21st Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA)

作者： Tsouvalas, Billy Nikiforakis, Nick SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9783031641701;9783031641718

In this paper, we introduce PageKnocker, a deceptionbased supplementary authentication mechanism, aimed primarily at protecting the public-facing authentication endpoints of critical web applications. PageKnocker is inspired by the network security concept of port knocking, and uses the requests to the web application as a means of authentication for the login page. Specifically, the authentication is successful (i.e. the user gets to access the login page of a web application), if the user's request sequence matches their personal predefined request sequence. In this manner, PageKnocker offers web application administrators the comparative advantage of knowing the nature of a visitor even before that visitor sends the first set of credentials. Alongside PageKnocker, we introduce two deceptive login environments, one overtly deceptive and one clandestine, towards which we direct any unauthenticated user attempting to reach the real login page. To evaluate the security and usability of page knocks, we deploy PageKnocker-protected honeypots in the wild and perform a separate user study, showing that PageKnocker can resist tens of thousands of brute-forcing bots, while remaining usable and intuitive.

关键词： Authentication

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18th International Conference on computer-Aided Design and computer Graphics (CAD/Graphics)

作者： He, Yu Jin, Yi-Han Liu, Ying-Tian Lu, Bao-Li Yu, Ge Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China Tianjin Univ Technol Tianjin Peoples R China Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China

ISBN: (数字)9789819996667

ISBN: (纸本)9789819996650;9789819996667

We introduce an automated pipeline for synthesizing texture maps in complex indoor scenes. With a style sample or color palette as inputs, our pipeline predicts theme color for each room using a GAN-based method, before generating texture maps using combinatorial optimization. We consider constraints on material selection, color correlation, and color palette matching. Our experiments show the pipeline's ability to produce pleasing and harmonious textures for diverse layouts and our contribution of an interior furniture texture dataset with 4,337 texture images.

关键词： Virtual Reality Texture Synthesis Color Correlation Style Harmony

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Visualization of ESG-Related Information in Integrated Reports Using Text Mining and Usefulness as Teaching Material

Visualization of ESG-Related Information in Integrated Repor...

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Conference on Human Interface and the Management of Information Thematic Area (HIMI) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Nakamura, Mizuki Akakura, Takako Tokyo Univ Sci Grad Sch Engn 6-3-1 NiijukuKatsushika Ku Tokyo 1258585 Japan Tokyo Univ Sci Fac Engn 6-3-1 NiijukuKatsushika Ku Tokyo 1258585 Japan

ISBN: (纸本)9783031601132;9783031601149

Recently, there has been a rapid increase in environmental, social, and governance (ESG) investments. According to the Global Sustainable Investment Alliance, during 2016-2020, the global level of ESG investments increased 1.5 times to approximately 1,364 trillion yen;in Japan, it surged six-fold to approximately 264 trillion yen. This surge has led to a growing interest in ESG-related information disclosed by companies. However, studies on visualizing and combining integrated reports of multiple companies are scarce. Therefore, this study visualized and examined the type of ESG-related information emphasized in integrated reports of the banking sector and identified future focal points and challenges. In addition, a questionnaire survey was conducted to validate the usefulness of the visualization. The results confirmed the possibility of efficient and rapid understanding of the focus areas of the entire industry using a large amount of textual data, such as integrated reports. Furthermore, the visualization results provide an opportunity to learn about specific methods for incorporating ESG into management and its strategic significance.

关键词： ESG Integrated Reports Text Mining

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A Multi-perspective Squeeze Excitation Classifier Based on Vision Transformer for Few Shot Image Classification 6th

A Multi-perspective Squeeze Excitation Classifier Based on V...

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6th Chinese Conference on Pattern Recognition and computer Vision (PRCV)

作者： Zhang, Zebao Li, Yuzhao He, Ming Harbin Engn Univ Harbin 150000 Peoples R China

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.

关键词： Few-shot learning Vision transformer Image classification

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GXJoin: Generalized Cell Transformations for Explainable Joinability 28th

GXJoin: Generalized Cell Transformations for Explainable Joi...

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28th European Conference on Advances in Databases and Information Systems (ADBIS)

作者： Omidvartehrani, Soroush Nobari, Arash Dargahi Rafiei, Davood Univ Alberta Edmonton AB Canada

ISBN: (纸本)9783031706288;9783031706264

Describing real-world entities can vary across different sources, posing a challenge when integrating or exchanging data. We study the problem of joinability under syntactic transformations, where two columns are not equi-joinable but can become equi-joinable after some transformations. Discovering those transformations is a challenge because of the large space of possible candidates, which grows with the input length and the number of rows. Our focus is on the generality of transformations, aiming to make the relevant models applicable across various instances and domains. We explore a few generalization techniques, emphasizing those that yield transformations covering a larger number of rows and are often easier to explain. Through extensive evaluation on two real-world datasets and employing diverse metrics for measuring the coverage and simplicity of the transformations, our approach demonstrates superior performance over state-of-the-art approaches by generating fewer, simpler and hence more explainable transformations as well as improving the join performance.

关键词： Generalization Explainable Join Transformation

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Decentralized Conflict Resolution for Navigation in Swarm Robotics 14th

Decentralized Conflict Resolution for Navigation in Swarm Ro...

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14th International Conference on Swarm Intelligence (ANTS)

作者： Mai, Sebastian Mostaghim, Sanaz Otto von Guericke Univ Chair Computat Intelligence Fac Comp Sci Magdeburg Germany Fraunhofer Inst Transportat & Infrastruct Syst IV Dresden Germany

ISBN: (纸本)9783031709319;9783031709326

The main challenge in multi-robot navigation is the resolution of navigation conflicts between agents caused by intersecting paths. The contributions of this paper are: The creation of a robot framework that allows us to study the emergent behavior of a swarm of robots in a navigation task that involves frequent conflicts, a baseline strategy for decentralized conflict resolution and a more advanced strategy called Decentralized Collective Conflict Resolution (DCCR). To resolve conflicts, the DCCR strategy uses communication between robots and collective decision-making to create planning constraints, while the baseline strategy creates implicit priorities from communication timing. To show the effectiveness of our methods, we use a swarm of up to ten Turtle-Bot3 robots applying the DCCR and the baseline strategies in simulated and real-world experiments. In scenarios with more than six robots, the baseline strategy is outperformed by the DCCR strategy, which has more resilience against congestion.

关键词： Multipurpose robots

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A Probabilistic Choreography Language for PRISM 26th

A Probabilistic Choreography Language for PRISM

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26th International Conference on Coordination Models and Language (COORDINATION)

作者： Carbone, Marco Veschetti, Adele IT Univ Copenhagen Copenhagen Denmark Tech Univ Darmstadt Darmstadt Germany

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.

关键词： Specification languages

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Augmented Reality for Enhanced Usability and Functionality of Industrial SCADA Systems

Augmented Reality for Enhanced Usability and Functionality o...

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International Conference on Extended Reality (XR Salento)

作者： Naranjo, Jose-E Ayala, Paulina Cruz-Cando, Jose Alban-Taipe, Mayra-S Garcia, Marcelo-, V Univ Tecn Cotopaxi UTC CIYA Campus La MatrizAve Simon Rodriguez Latacunga 050102 Ecuador Univ Tecn Ambato UTA FISEI Ambato 180206 Ecuador Univ Basque Country EHU UPV DISA Bilbao 48013 Spain

ISBN: (纸本)9783031717093;9783031717109

This research focuses on the integration of augmented reality (AR) technology into Supervisory Control and Data Acquisition (SCADA) systems for industrial applications. A SCADA system in an AR environment was developed, enabling users to monitor and control industrial processes through an immersive and interactive interface. The system's architecture and implementation methodology are described, including communication protocols, data exchange, and 3D object manipulation. A mobile application was also implemented to enhance connectivity and accessibility. The usability and functionality of the AR SCADA system were validated through user surveys and statistical analysis. The results demonstrated that the application meets established usability parameters, providing an intuitive interface and satisfactory performance in controlling and monitoring industrial variables. The successful integration of AR into SCADA systems presents opportunities to enhance operational efficiency and safety in industrial environments. This study highlights the potential of AR technology in modernizing industrial control systems and paves the way for further advancements in this field.

关键词： Augmented Reality (AR) SCADA Systems Industrial Automation Human-Machine Interfaces Usability Evaluation

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Users' Preference-Aware Music Recommendation with Contrastive Learning 20th

Users' Preference-Aware Music Recommendation with Contrastiv...

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20th International Conference on Intelligent Computing (ICIC)

作者： Wang, Jian Ma, Huifang Northwest Normal Univ Conservatory Mus Lanzhou 730070 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9789819756148;9789819756155

Music recommendation serves as a critical branch of recommender systems, which is pivotal to capture users' preference via their historical listening sequence for improving user experience. However, existing efforts in music domain acquire users' preference in a supervised manner, thus inevitably suffering from the problem of data sparsity due to rare interactions between users and music pieces and further failing to precisely model users' preference. Inspired by the recent success of self-supervised learning, in this paper, we propose a novel Users' Preference-aware Music recommendation with Contrastive Learning (UPMCL) method to mitigate the above issues. To be specific, the proposed approach UPMCL first encodes the information of music pieces according to original and augmented listening sequences. Moreover, it employs the contrastive learning to maximize the agreement between mask- and permute-based augmented listening sequences to learn the representations of music pieces. Eventually, the attention mechanism is utilized to integrate different types of users' preferences to generate the comprehensive users' preference and further achieve accurate music recommendation. Extensive experimental results conducted on three real-world music datasets clearly demonstrate that UPMCL has the capability in effectively recommending appropriate music pieces to users.

关键词： Recommendation Systems Music Recommendation Users' Preference Contrastive Learning

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Deformable Medical Image Registration Under Distribution Shifts with Neural Instance Optimization 14th

Deformable Medical Image Registration Under Distribution Shi...

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14th International Workshop on Machine Learning in Medical Imaging (MLMI)

作者： Mok, Tony C. W. Li, Zi Xia, Yingda Yao, Jiawen Zhang, Ling Zhou, Jingren Lu, Le Alibaba Grp DAMO Acad Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

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.

关键词： Degrees of freedom (mechanics)

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