Abstract--As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. The Controller Area Network OBD-II diagnostic data is easy to access via the OBD-II port, (CAN) protocol was firstly developed by Bosch in the as all modern cars are equipped with the OBD-II diagnostic 1980s [1] and serves as the de facto standard protocol for connecting system. OBD-II diagnostic data can be converted into humanreadable ECUs embedded in cars [3]-[5]. The standard structure accurate vehicle data with public formulas to be used of the CAN frame is composed of the start of frame, arbitration in the matching process for associating semantic meanings field, control field, data field, CRC field, ACK field and end with CAN signals. Both OBD-II diagnostic data and regular of frame, as shown in Figure 1. While the CAN protocol has CAN frames can be collected from the OBD-II port. The a standardized frame structure, understanding the protocol's RE systems can leverage both CAN and OBD-II diagnostic utilization for signal transmission remains challenging. This data to create a comprehensive dataset for reverse engineering is because Original Equipment Manufacturers (OEMs) encode purposes, eliminating the need for additional measurement the signals within the CAN frames' data fields (data payloads) equipment like IMUs. in proprietary ways that vary among OEMs, vehicle models, The primary objective of a CAN RE system is to identify the and years [6]. CAN messages frames is the first step to extracting the essential information are structured into frames, and the CAN frames of different to develop autonomous applications or explore automotive CAN IDs have different lengths of the data payload.

Karol Antczak Military University of Technology in WarsawInstitute of Computer and Information SystemsABSTRACT We propose a representation learning frameworkfor medical diagnosis domain. It is based on hetero - geneous network-based model of diagnostic data aswell as modified metapath2vec algorithm forlearning latent node representation. We comparethe proposed algorithm with other representationlearning methods in two practical case studies:symptom/disease classification and disease predic - tion. We observe a significant performance boost inthese task resulting from learning representationsof domain data in a form of heterogeneous network. INTRODUCTIONRepresentation learning is a group of machinelearning methods that aims to find useful represen - tations of the data. The "usefulness" is typicallyunderstood in terms of extraction of features thatare meaningful from the point of view of targetobjective.

Medical diagnostic services using artificial intelligence are set to be introduced this year in Japan, where the adoption of information technology in health care has been slow, sources said Monday. The private Showa University in Tokyo together with Nagoya University and Cybernet Systems Co. have jointly developed software that analyzes colon polyps shown in images taken during endoscopic examinations. Using a vast amount of past diagnostic data, the software can determine whether such polyps are malignant. It has been proven reliable -- with an assessment determining that it can identify a potentially cancerous polyp with the same accuracy as a leading specialist -- and approved for commercialization by the government. LPixel Inc., an image analysis service venture, has produced a program that spots cell degeneration in the cerebrum by examining images taken during a magnetic resonance imaging (MRI) scan.