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Successful communication is thought to require members of a speech community to learn common mappings between words and their referents. But if one person’s concept of CAR is very different from another person’s, successful communication might fail despite the common mappings because different people would mean different things by the same word. Here we investigate the possibility that one source of representational alignment is language itself. We report a series of neural network simulations investigating how representational alignment changes as a function of agents having more or less similar visual experiences (overlap in “visual diet”) and how it changes with exposure to category names. We find that agents with more similar visual experiences have greater representational overlap. However, the presence of category labels not only increases representational overlap, but also greatly reduces the importance of having similar visual experiences. The results suggest that ensuring representational alignment may be one of language’s evolved functions. 

Abstract A simple and environmentally‐friendly approach was developed to synthesize 2D CuO nanosheets using electrochemical deposition. The formed 2D CuO nanosheets (NSs) exhibit numerous advantageous properties such as no toxicity, high electrical conductivity, large active surface area, and a p‐type semiconducting nature with a band gap of 1.2 eV. A sensitive electrochemical sensor was constructed for the amperometric detection of glucose to take advantage of these characteristics. The fabricated sensor displayed an excellent sensitivity of 2710 μA mM⁻¹ cm⁻²along with a wide linear range of 0.001–1.0 mM and a lower limit of detection of 0.8 μM (S/N=3). Additionally, the modified electrode possesses high selectivity and good stability. The outstanding electrocatalytic performance of the electrode is attributed to a large active surface area, unique structural morphology, and the high conductivity of the 2D CuO nanosheets. 

Mitigating vulnerabilities in industrial control systems (ICSs) represents a highly complex task. ICSs may contain an abundance of device types, all with unique software and hardware components. Upon discovering vulnerabilities on ICS devices, cyber defenders must determine which mitigations to implement, and which mitigations can apply across multiple vulnerabilities. Cyber defenders need techniques to optimize mitigation selection. This exploratory research paper shows how ontologies, also known as linked-data models, can potentially be used to model ICS devices, vulnerabilities, and mitigations, as well as to identify mitigations that can remediate or mitigate multiple vulnerabilities. Ontologies can be used to reduce the complexity of a cyber defender’s role by allowing for insights to be drawn, especially in the ICS domain. Data are modelled from the Common Platform Enumeration (CPE), the National Vulnerability Database (NVD), standardized list of controls from the National Institute of Standards and Technology (NIST), and ICS Cyber Emergency Response Team (CERT) advisories. Semantic queries provide the techniques for mitigation prioritization. A case study is described for a selected programmable logic controller (PLC), its known vulnerabilities from the NVD, and recommended mitigations from ICS CERT. Overall, this research shows how ontologies can be used to link together existing data sources, to run queries over the linked data, and to allow for new insights to be drawn for mitigation selection. 

Ethical hacking consists of scanning for targets, evaluating the targets, gaining access, maintaining access, and clearing tracks. The evaluation of targets represents a complex task due to the number of IP addresses, domain names, open ports, vulnerabilities, and exploits that must be examined. Ethical hackers synthesize data from various hacking tools to determine targets that are of high value and that are highly susceptible to cyber-attacks. These tasks represent situation assessment tasks. Previous research considers situation assessment tasks to be tasks that involve viewing an initial set of information about a problem and subsequently piecing together more information to solve the problem. Our research used semantic-web technologies, including ontologies, natural language processing (NLP), and semantic queries, to automate the situation assessment tasks conducted by ethical hackers when evaluating targets. More specifically, our research focused on automatically identifying education organizations that use industrial control system protocols which in turn have highly exploitable vulnerabilities and known exploits. We used semantic-web technologies to reduce an initial dataset of 126,636 potential targets to 155 distinct targets with these characteristics. Our research adds to previous research on situation assessment by showing how semantic-web technologies can be used to reduce the complexity of situation assessment tasks.