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Semantic relationships, such as hyponym–hypernym, cause–effect, meronym–holonym etc., between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations—hyponym–hypernym, cause–effect, and meronym–holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER’s performance is substantially superior. 

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics do not provide adequate energy security. As a result, researchers have focused their efforts resilience metrics to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose an operational resilience metric for modern power distribution systems. The metric is based on the aggregation of system assets adaptive capacity in real and reactive power. This metric gives information to the magnitude and duration of a disturbance the system can withstand. We demonstrate resilience metric in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages. 

Motivated by decreased cost and climate change concerns, the penetration of solar photovoltaic (PV) energy generation and battery energy storage has been continually increasing. The variability in solar PV power generation has led to many new challenges for utilities and researchers. One challenge is the quantification of the resilience contribution to the grid from its assets and is the topic of this paper. In this work, we propose a framework for evaluating the resilience contribution of solar generation and battery storage assets on the grid. The metric provides a quantifiable adaptive capacity measure in terms of real and reactive power and includes uncertainty for solar PV assets. A case study using very short-term and short-term solar generation forecast demonstrates the framework and provides useful insight to the resilience solar and battery storage assets can contribute to the grid. 

Industrial Control Systems (ICS) are the brain and backbone of nation's critical infrastructure such as nuclear power, water treatment, and petrochemical plants. In order to increase interoperability, real-time availability of data, and flexibility, information/communication technologies are adopted in this domain. While these information technologies have been effective, they are integrated into operational technologies without the necessary security defense. Designing an effective, layered security defense is not possible unless security threats are identified through a structural analysis of the ICS. For that reason, this paper provides an attacker's point of view on the reconnaissance effort necessary to gather details of the system dynamics - which are required for the development of sophisticated attacks. We present a reconnaissance approach which uses the system's I/O data to infer the dynamic model of the system. In this effort, we propose a novel cyber-attack which targets the controller proportional-integral-derivative gain values in a constant setpoint control system. Our findings will help researchers design more secure control systems.