Artificial Intelligence

Scene text detection and recognition have attracted much attention in recent years because of their potential applications. Detecting and recognizing texts in images may suffer from scene complexity and text variations. Some of these problematic cases are included in popular benchmark datasets, but only to a limited extent. In this work, we investigate the problem of scene text detection and recognition in a domain with extreme challenges.

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Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in  network slicing  (NetS) technology.This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS.

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Volkswagen Group of America Innovation and Engineering Center California (VW IECC) is a research facility in Belmont, California working on the future of the mobility. In the recent years exciting developments have happened for the autonomous vehicles. In general, lack of data is the main problem to tackle to solve the task of autonomous driving. One of the important tasks in this topic is the overtaking and lane changes, especially in the highway scenarios.

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In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. Here we introduce a novel dataset for intrusion detection in Software-Defined Networking named SDNFlow. The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities.

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The UNSW-NB15 Dataset is a compilation of raw network data packets crafted by the University of South Wales. This dataset is designed to create a blend of modern normal network activity and synthetic contemporary attack behavior. It encompasses nine types of attacks, including Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms, resulting in a total of 10 classes of traffic and 49 features.

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The prognostic survival dataset, Pancreatic Cancer Survival based on Preoperative Features (PCSPF), was constructed to explore the impact of key preoperative features on prognosis based on the follow-up data of patients with pancreatic cancer at Changhai Hospital, Shanghai, China.

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Captcha stands for Completely Automated Public Turing Tests to Distinguish Between Humans and Computers. This test cannot be successfully completed by current computer systems; only humans can. It is applied in several contexts for machine and human identification. The most common kind found on websites are text-based CAPTCHAs.A CAPTCHA is made up of a series of alphabets or numbers that are linked together in a certain order.

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1.    The release data includes the original data of wind turbine X30 at 2018 and 2019, which are files “2019_x30.csv” and “2018_x30.csv” in directory “Original data”.    For the turbine, the wind speed and direction are collected at each 30 seconds, then there are 2880 data at a day. In the experiment, we use the slide window with 120 data, which corresponding to an hours, and slide the window with 10 data step, which corresponding to 5 minutes. For the data in window, we select the data of final 10 minutes as the label.

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Complex networks exhibit inherent community structures that contain rich topology information of graphs. Existing graph neural networks (GNNs) have not fully utilized community structures and integrating them into GNNs has the potential to enhance nodes' representation capabilities and prediction performance. In this paper, we propose a community-aware graph neural network (CAG), which designs a community subgraph explorer (CSE) that leverages monte carlo tree search (MCTS) to select the most informative subgraphs within communities.

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The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.

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