Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called "SafeAccess+" to build safer and ADA compliant premises (e.g. smart homes, offices). The key functionalities of the SafeAccess+ are: 1) Monitoring the inside/outside of premises and identifying incoming people; 2) Providing users relevant information to assess incoming threats (e.g., burglary, robbery) and ongoing crimes 3) Allowing users to grant safe access to homes for friends/family members. We have addressed several technical and research challenges: - developing models to detect and recognize person/activity, generating image descriptions, designing ADA compliant end-end system. In addition, we have designed a prototype smart door showcasing the proof-of-concept. The premises are expected to be equipped with cameras placed in strategic locations that facilitate monitoring the premise 24/7 to identify incoming persons and to generate image descriptions. The system generates a pre-structured message from the image description to assess incoming threats and immediately notify the users. The completeness and generalization of models have been ensured through a rigorous quantitative evaluation. The users' satisfaction and reliability of the system has been measured using PYTHEIA scale and was rated excellent (Internal Consistency-Cronbach's alpha is 0.784, Test-retest reliability is 0.939 )

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work, which has looked into claim retrieval, here we take a document-level perspective. We create a new manually annotated dataset for the task, and we propose suitable evaluation measures. We further experiment with a learning-to-rank approach, achieving sizable performance gains over several strong baselines. Our analysis demonstrates the importance of modeling text similarity and stance, while also taking into account the veracity of the retrieved previously fact-checked claims. We believe that this research would be of interest to fact-checkers, journalists, media, and regulatory authorities.

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.

The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weather. In recent years, many research efforts have been proposed for forecasting multivariate time series. Although some previous work considers the interdependencies among different variables in the same timestamp, existing work overlooks the inter-connections between different variables at different time stamps. In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast. We conduct experiments on the Traffic, Electricity, and Exchange-Rate multivariate time series datasets. The results show that our proposed model outperforms the state-of-the-art baseline methods.

Penetration testing the organised attack of a computer system in order to test existing defences has been used extensively to evaluate network security. This is a time consuming process and requires in-depth knowledge for the establishment of a strategy that resembles a real cyber-attack. This paper presents a novel deep reinforcement learning architecture with hierarchically structured agents called HA-DRL, which employs an algebraic action decomposition strategy to address the large discrete action space of an autonomous penetration testing simulator where the number of actions is exponentially increased with the complexity of the designed cybersecurity network. The proposed architecture is shown to find the optimal attacking policy faster and more stably than a conventional deep Q-learning agent which is commonly used as a method to apply artificial intelligence in automatic penetration testing.

Most of the public discourse around artificial intelligence (AI) policy focuses on one of two perspectives: how the government can support AI innovation, and how the government can deter its harmful or negligent use. Yet there can also be a role for government in making it easier to use AI beneficially--in this niche, the National Science Foundation (NSF) has found a way to contribute. Through a grant-making program called Fairness in Artificial Intelligence (FAI), the NSF is providing $20 million in funding to researchers working on difficult ethical problems in AI. The program, a collaboration with Amazon, has now funded 21 projects in its first two years, with an open call for applications in its third and final year. This is an important endeavor, furthering a trend of federal support for the responsible advancement of technology, and the NSF should continue this important line of funding for ethical AI.

Highly realistic deepfake videos didn't quite make the splash some feared they would during the 2020 presidential election. Nevertheless, deepfakes are causing trouble--for regular people. In March, the Federal Bureau of Investigation warned that it expected fraudsters to leverage "synthetic content for cyber … operations in the next 12-18 months." In deepfake videos, which first appeared in 2017, a computer-generated face (often of a real person) is superimposed on someone else. After the swap, the fraudsters can make the target person say or do just about anything.

The US Federal Trade Commission (FTC) warns of extortion scammers targeting the LGBTQ community via online dating apps such as Grindr and Feeld. As the FTC revealed, the fraudsters would pose as potential romantic partners on LGBTQ dating apps, sending explicit photos and asking their targets to reciprocate. If they fall for the scammers' tricks, the victims will be blackmailed to pay a ransom, usually in gift cards, under the threat of leaking the shared sexual imagery with their family, friends, or employers. "To make their threats more credible, these scammers will tell you the names of exactly who they plan to contact if you don't pay up. This is information scammers can find online by using your phone number or your social media profile," the FTC said.

They may seem like something out of The Avengers film franchise, but these ambitious concepts of revolutionary warships are actually part of the Royal Navy's vision of what the British fleet could look like in the future. Detailed proposals for four potential vehicles, created by young engineers, have been released, including a stealth submarine carrier and a huge flying drone station which would be attached to a helium balloon and based in the stratosphere. The idea is that attack drones shaped like conventional airplanes could then be launched from the station'at a moment's notice' before shooting down towards Earth and potentially gliding just beneath the water in a stealth mode and smashing into an enemy ship. The Royal Navy hasn't disclosed anticipated costs of bringing to life the newly-revealed concepts, which have been described as one expert involved in British defence and security operations as very much'in the realm of speculative thinking'. They have been put forward by young engineers from industry and academia as part of a challenge posed by the UK Naval Engineering Science and Technology (UKNEST), aimed at helping the Royal Navy to develop ideas for an autonomous fleet that could shape how it operates over the next 50 years.

An Israeli defense contractor on Monday unveiled a remote-controlled armed robot it says can patrol battle zones, track infiltrators and open fire. The unmanned vehicle is the latest addition to the world of drone technology, which is rapidly reshaping the modern battlefield. Proponents say such semi-autonomous machines allow armies to protect their soldiers, while critics fear this marks another dangerous step toward robots making life-or-death decisions. The four-wheel-drive robot presented Monday was developed by the state-owned Israel Aerospace Industries' "REX MKII." It is operated by an electronic tablet and can be equipped with two machine guns, cameras and sensors, said Rani Avni, deputy head of the company's autonomous systems division.