Software-as-a-Service (SaaS) providers increasingly rely on multi-cloud setups to leverage the combined benefits of different enabling technologies and third-party providers. Especially, in the context of NoSQL storag...
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Software-as-a-Service (SaaS) providers increasingly rely on multi-cloud setups to leverage the combined benefits of different enabling technologies and third-party providers. Especially, in the context of NoSQL storage systems, which are characterized by heterogeneity and quick technological evolution, adopting the multi-cloud paradigm is a promising way to deal with different data storage requirements. Existing data access middleware platforms that support this type of setup (polyglot persistence) commonly rely on (i) configuration models that describe the multi-cloud setup, and (ii) the hard-coded logic in the application source code or the data storage policies that define how the middleware platforms should store data across different storage systems. In practice, however, both models are tightly coupled, i.e. the hard-coded logic in the application source code and data storage policies refer to specific configuration model elements, leads to fragility issues (ripple effects) and hinders reusability. More specifically, in multi-cloud configurations that change often (e.g., in dynamic cloud federations), this is a key problem. In this paper, we present a more expressive way to specify storage policies, that involves (i) enriching the configuration models with metadata about the technical capabilities of the storage systems, (ii) referring to the desired capabilities of the storage system in the storage policies, and (iii) leaving actual resolution to the policy engine. Our validation in the context of a realistic SaaS application shows how the policies accommodate such changes for a number of realistic policy change scenarios. In addition, we evaluate the performance overhead, showing that policy evaluation is on average less than 2% of the total execution time.
the present contribution aims to describe the development of a serious game designed to disseminate knowledge in the field of architecture and archeology. In recent decades, educational video games, or serious games, ...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
the present contribution aims to describe the development of a serious game designed to disseminate knowledge in the field of architecture and archeology. In recent decades, educational video games, or serious games, have emerged as a significant means of engaging students across various educational disciplines. Notably, some of these games have shown considerable potential in conveying boththe tangible and intangible aspects of cultural heritage, particularly within the teaching of historical subjects. the case study under consideration involves the initial phase of a game focused on the reconstruction of one corner of the Temple of Olympian Zeus in the Archeological Park of Agrigento. Following an evaluative test will be conducted with high school and first year university students, in order to gather data generated by its use.
this study explores feature selection for classifying galaxy morphology using the extensive Galaxy Zoo 2 dataset. We investigate supervised and unsupervised learning methods to group galaxies based on key features, ai...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
this study explores feature selection for classifying galaxy morphology using the extensive Galaxy Zoo 2 dataset. We investigate supervised and unsupervised learning methods to group galaxies based on key features, aiming to replicate supervised learning results. We evaluate various feature selection methods and compare them to an existing classification approach. Our results demonstrate that a reduced set of features based on adjusted vote fractions improves classification accuracy and potentially reduces computational complexity. While unsuper-vised clustering partially groups galaxies by morphology, further optimization is required. this work suggests that feature selection and unsupervised learning are promising techniques for the efficient classification of large galaxy datasets in upcoming astronomical surveys.
In this work, we focus on variants of Subset Sum. We first define a new variant called the Unique Projection Subset Sum (u – PSSUM) problem: Given $\left(a_th, \ldots, a_{n}\right) \in \mathbb{Z}_{\geq 0}^{n}$, such...
In this work, we focus on variants of Subset Sum. We first define a new variant called the Unique Projection Subset Sum (u – PSSUM) problem: Given $\left(a_th, \ldots, a_{n}\right) \in \mathbb{Z}_{\geq 0}^{n}$, such that for every $t, \sum_{i \in[n]} c_{i} a_{i}=t$, for $c_{i} \in\{0,1\}$ has unique solution if exists, u-PSSUM asks for a vector $\vec{x}=\left(x_th, \ldots, x_{n}\right)$, such that there exists an $S \subseteq[n]$, where $\sum_{i \in S} x_{i}=t$, and $\|\vec{a}-\vec{x}\|_{p}$ is minimized, where $\|\cdot\|_{p}$ denotes the $\ell_{p}$ norm. We present a deterministic $O(n t)$-time algorithm and a randomized $\widetilde{O}(n+t)$-time algorithm for u-PSSUM, in $\ell_th$-norm. the second one is already a known variant called Un-bounded Subset Sum (UBSSUM) problem, which takes a tuple of non-negative integers $\left(a_th, \ldots, a_{n}, t\right)$, as an input, and asks whether there exists non-negative integers $\beta_th, \ldots, \beta_{n}$, such that $\sum_{i=1}^{n} \beta_{i} a_{i}=t$. We present the first polynomial time reductions from UBSSUM to Closest Vector Problem (CVP) in $\ell_th$ and $\ell_{\infty}$ norms. We also give new algorithms for two variants of UBSSUM problem.
Phishing emails are among today's most common attack vectors since most enterprises and consumer users still rely on email for day-to-day operations. At the same time, some functionalities (such as MACRO support a...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
Phishing emails are among today's most common attack vectors since most enterprises and consumer users still rely on email for day-to-day operations. At the same time, some functionalities (such as MACRO support and Visual Basic Programming) available in Microsoft Office Suite make it easier for an attacker to inject malicious code into a document that can be sent via email. From a research perspective, analyzing such an attack implies understanding not only the artifacts that compose the kill chain steps but also being able to extract, from a document, the embedded macros and the scripts. this paper focuses on building such a system using the forensics platform GView by adding various plugins and functionalities designed to augment the existing support and allow a security researcher to analyze the content of an email and attached documents quickly.
Artificial Intelligence has the potential to streamline and facilitate numerous processes in the medical field, increasing the quality of life for millions and potentially saving lives. One area which requires a lot o...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
Artificial Intelligence has the potential to streamline and facilitate numerous processes in the medical field, increasing the quality of life for millions and potentially saving lives. One area which requires a lot of effort when it comes to diagnosing severe diseases is Histopathology. Recently, a modern learning strategy, Multitask learning, was applied in Digital Histopathology in order to obtain relevant medical information from histological images. this paradigm is able to increase performance by learning multiple objectives simultaneously resulting in more general features. the resulting methods reduce overfitting and computational complexity while increasing data efficiency making it a suitable choice for the high-dimensionality, low sample size sets from the medical field. the aim of this work is to present novel multitask approaches with applications in Histopathology, analyse them, showcase their advantages and drawbacks and identify possible future research directions.
the number of known malicious samples has increased exponentially in the last decade, making it more complicated for a security researcher to identify and cluster them quickly. While for most scenarios, most data view...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
the number of known malicious samples has increased exponentially in the last decade, making it more complicated for a security researcher to identify and cluster them quickly. While for most scenarios, most data viewers relate to the file type (either binary or textual) to extract meaningful data, the protective measurements of different malicious payloads may make this task difficult. Our research introduces an approach that develops an enhanced visualization mode within the open-source framework GView to address this challenge. It harnesses established entropy-based analytical principles to facilitate the identification of anomalies and intrinsic properties in binary data, irrespective of specific file formats. this methodology streamlines threat evaluation and contributes to the broader field of cybersecurity by offering a building block for a scalable solution for analyzing the everexpanding volumes of digital information.
Disassembly is the process that translates machine code into a higher-level and more human-readable form. From the security analysis and malware detection perspective, it is an essential technique. However, traditiona...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
Disassembly is the process that translates machine code into a higher-level and more human-readable form. From the security analysis and malware detection perspective, it is an essential technique. However, traditional disassembly tools often display the information in a textual format, thus making it difficult to navigate and understand. In this paper, we explore the concepts involved in creating a viewer for disassembly that focuses on static analysis and boosts the user experience like clearly displaying the code, showcasing relevant parts, guiding the user, offering an interactive experience, map data structures and seamless integration with other tools. Furthermore, we emphasize the importance of having both a user-friendly and also customizable interface without having to concede on overall speed and performance. In this manner, even a novice in security analysis could have a positive experience. Considering all these features, the final result leads to a more productive and efficient analysis.
One key component of a cyberattack is malware. If a mal ware program is detected and blocked the cyberattack may stagger or fail, thus bad actors design their mal ware programs with additional characteristics and func...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
One key component of a cyberattack is malware. If a mal ware program is detected and blocked the cyberattack may stagger or fail, thus bad actors design their mal ware programs with additional characteristics and functionalities that harden the analysis of the mal ware program and make the malware undetectable by antivirus solutions. Withthe appearance of more advanced malware new detection methods are needed. Withthe help of reverse engineering techniques and software engineering concepts, one model that analysts can work with is Control Flow Graphs. Used for software optimization, control-flow-graphs offer the advantage of graph properties for analysts to detect malicious particularities in malware samples. this paper explores some methods of detection and analysis based on control flow graphs, categorizes them in four categories and highlights different particularities in these approaches.
Lung cancer persists as a global leader in cancer-related deaths, highlighting the critical need for precise and efficient detection methods. this paper investigates the use of the Medical Segmentation Decathlon datas...
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ISBN:
(数字)9798331532833
ISBN:
(纸本)9798331532840
Lung cancer persists as a global leader in cancer-related deaths, highlighting the critical need for precise and efficient detection methods. this paper investigates the use of the Medical Segmentation Decathlon dataset to train neural networks for lung cancer segmentation in CT scans via semantic segmentation. We propose and evaluate four new data adaptation techniques specifically designed for this dataset, with each technique being assessed using U-Net-based architectures. Our approach incorporates a thorough exploratory data analysis to uncover the dataset's strengths and weaknesses, which in turn guided our data preprocessing and augmentation strategies.
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