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Question Answer Pair dataset for Sentiment Analysis Tasks.
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The dataset is generated by performing different Man-in-the-Middle (MiTM) attacks in the synthetic cyber-physical electric grid in RESLab Testbed at Texas AM University, US. The testbed consists of a real-time power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master, SEL real-time automation controller (RTAC), and Cisco Layer-3 switch. With different scenarios of MiTM attack, we implement a logic-based defense mechanism in RTAC and save the traffic data and related cyber alert data under the attack.
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The Bitcoin Lightning Network (LN) disrupts the scenario as a fast and scalable method to make payment transactions off-chain, alongside the Bitcoin network, thereby reducing the on-chain burden. Understanding the topology of the LN is crucial, not only because it is key to performance, but also for ensuring its security and privacy guarantees. The topology of the LN affects, among others, the ability to successfully route payments between nodes, its resilience (against both attacks and random failures), and the privacy of payments.
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This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.
This images were inicially collected to support a robotic autonomous pollination project.
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This dataset was created by gathering "attack stories" related to IoT devices from the cybersecurity news site Threatpost. Because there aren't many databases of IoT vulnerabilities, we used Threatpost as an index to recent vulnerabilities, which we then researched using a variety of sources, like academic papers, blog posts, code repositories, CVE entries, government and vendor advisories, product release notes, and whitepapers.
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WannaCry Bitcoin Cash-in and Cash-out payment network data in JSON along with STIX representation of address 12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw
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This is the dataset for the paper Bayesian Inference of Sector Orientation in LTE Networks based on End-User Measurements published at VTC 2021 - Fall.
It includes a set of Drive-Test RSRP Pathloss Measurements with their relative position to the corresponding eNodeB. In total it contains data for 91 three-sector eNodeBs, which results in 273 sectors.
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Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks.
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The time-to-market pressure and the continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to imp
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