Word feature vectors have been proven to improve many NLP tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much more data, which also resulted in better quality of learned features. Since it learns joint probability of latent features of words, it has the advantage that we can train it without any prior knowledge about the goal task we want to solve. We aim to evaluate the universal applicability property of feature vectors, which has been already proven to hold for many standard NLP tasks like part-of-speech tagging or syntactic parsing. In our case, we want to understand the topical focus of text documents and design an efficient representation suitable for discriminating different topics. The discriminativeness can be evaluated adequately on text categorisation task. We propose a novel method to extract discriminative keywords from documents. We utilise word feature vectors to understand the relations between words better and also understand the latent topics which are discussed in the text and not mentioned directly but inferred logically. We also present a simple way to calculate document feature vectors out of extracted discriminative words. We evaluate our method on the four most popular datasets for text categorisation. We show how different discriminative metrics influence the overall results. We demonstrate the effectiveness of our approach by achieving state-of-the-art results on text categorisation task using just a small number of extracted keywords. We prove that word feature vectors can substantially improve the topical inference of documents' meaning. We conclude that distributed representation of words can be used to build higher levels of abstraction as we demonstrate and build feature vectors of documents.

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

Afterward, we offer a solution that is capable of 1) making our AI systems robust against deepfakes during development and deployment phases; 2) detecting video, image, audio, and textual deepfakes; 3) identifying deepfakes that bypass detection (deepfake hunting); 4) leveraging available intelligence for timely identification of deepfake campaigns launched by state-sponsored hacking teams; 5) conducting in-depth forensic analysis of identified deepfake payloads. Our proposed solution can be used as a technical guide for developing detection, deterrence, and forensics investigation solutions for deepfakes. Our solution would address important elements of Canada's National Cyber Security Action Plan (2019-2024) in increasing the trustworthiness of our critical services [5]. Following actions can be taken based on this research findings: Raising public awareness about risks of deepfakes: increasing the understanding of deepfake threats and empowering Canadian public to do their part in keeping our society and critical services safe from deepfake-based attacks is the most important and effective step in reducing risk of deepfakes. Cybersecurity should always be considered as a shared responsibility. While this paper is focused on development of technical solutions for early detection and deterrence of deepfakes, the effectiveness of our solutions (or any technical solution in cybersecurity) are limited without regular and systemic public awareness campaigns. Supporting development of public training programs in this domain should be considered as a top priority. Developing AI robustness monitoring solutions: there is a growing trend in using AI to detect deepfakes. However, more recently, adversaries made attempts to create adversarial deepfake payloads that are capable of deceiving humans while bypassing AI-based detection systems!

Today's Wales-USA World Cup match. Things with which this World Cup is laden so far: Geopolitical intrigue and controversy. FIFA is touting an AI-powered decision-making system that will use sensors in the actual soccer ball to help determine calls. A vast network of facial recognition-enabled cameras will track the crowd, with technology in the same family as that deployed by the controversial firm Clearview AI. AI-powered sensors in the stadiums will even help control the climate.

This report focuses on the risks related to the potential lack of predictability of AI systems – referred to as the predictability problem – and its implications for the governance of AI systems in the national security domain. Predictability of AI systems indicates the degree to which one can answer the question: what will an AI system do? The predictability problem can refer both to correct and incorrect outcomes of an AI system, as the issue is not whether the outcomes follow logically from the working of the system, but whether it is possible to foresee them at the time of deployment. In this report, we first analyse the predictability problem from technical and socio-technical perspectives and then focus on relevant UK, EU and US policy to consider whether and how they address this problem. From a technical perspective, we argue that given the multi- faceted process of design, development, and deployment of an AI system, it is not possible to account for all sources of errors or emerging behaviours that could result.

The UK government will amend its Online Safety Bill with measures designed to prohibit abuse of intimate images, whether or not they're real. If the bill becomes law as is, it will be illegal to share deepfake porn without the subject's consent. This would be the first ban on sharing deepfakes in the country and if the law comes into effect, violating this rule could lead to a prison sentence. Additionally, the Ministry of Justice aims to ban "downblousing," which it describes as an incident "where photos are taken down a woman's top without consent." The country banned upskirt photos, which are exactly what the term suggests, in 2019.

A "Festival of Robotics" conjured images of dancing androids and canape-serving cyborgs, or at least one of those Boston Dynamics monstrosities that resembles a fleshless Terminator but moves like a gymnast. Held on a wet day at BT's Adastral Park R&D facility, it did feature one of Boston Dynamics' mechanized dogs, which performed some lively robot dressage before it scampered off at pitbull speed, presumably on a kill mission. But there was not much festival atmosphere. "The rain has had a squashing impact on our ability to have a beer tent and open summer garden, but we will have dancing and other exciting things such as robot wars," said a spokesperson at a mid-morning presentation. Perhaps the robots came out to dance and fight in the evening, long after reporters had departed.

Over the past years, Russian search and tech giant Yandex made an effort not to fall behind its Western counterparts and had developed its own smart devices, self-driving cars, as well as its own food delivery and ride-sharing services, among other products. According to The New York Times, though, the West's sanctions against its home country after the invasion of Ukraine has made it impossible to continue developing and improving its projects. That's why Yandex's parent firm, which is registered in Amsterdam, is reportedly looking to sell and sever ties with Russia. Apparently, Yandex is planning to sell the emerging technologies it's working on to markets outside the country, since they require Western technologies and experts to reach their full potential. It's also looking to sell its established businesses, such as its internet browser, its food delivery and its ride-hailing apps.

This Digital Health News industry roundup includes an IT award for the Department of Health and Social Care and Netcompany for the NHS Covid Pass, accreditation for an AI device and a virtual falls service keeping care home residents out of hospital. Digital health and AI company Empatica has received clearance of its Empatica Health Monitoring Platform by the US Food and Drug Administration (FDA). The platform has been cleared for continuous data collection to monitor blood oxygen saturation during rest, peripheral skin temperature, activity associated with movement during sleep and electrodermal activity. Each digital biomarker is based on trained algorithms that analyse sensor data in one-minute intervals. Dr. Marisa Cruz, chief medical officer of Empatica, said: "This clearance represents a significant step forward for our scientific community. Patients, healthcare providers, and researchers deserve digital health products that are accurate, validated in diverse populations, and intuitive to use. "We are proud to have built a solution that accomplishes these goals, offering a high-quality and reliable digital health tool to scientists working to improve patient outcomes through research and clinical care." The company has also announced the closing of its Series B financing. The investment was led by Sanofi Venture and RA Capital Management with participation by Black Opal Ventures. Empatica intends to use the financing to expand its suite of digital biomarkers for use in both patient care and in clinical trials as digital endpoints. Cris De Luca, partner at Sanofi Ventures and newly-appointed board member at Empatica, said: "By gaining higher resolution into disease symptomology through novel digital measures and digital biomarkers in clinical and real-world settings, Empatica is unlocking the possibilities of early disease detection, enhanced treatment decisions, and improving quality of life for patients around the world." International IT services company, Netcompany, alongside the Department for Health and Social Care (DHSC) have won the Emerging Technology of the Year award in the Technology Excellence category of the 2022 UK IT Industry Awards. The two companies were recognised for their work on the NHS Covid Pass, which also saw them receive a highly commended in the Best Healthcare IT Project of the Year 2022. The win reflects the vital role that the NHS Covid Pass has played in the safe reopening of the country. It allows users to shared their Covid-19 status or vaccination status when travelling internationally. Richard Davies, UK country managing partner at Netcompany, said: "This award recognises our talented teams, expertise, and dedication towards creating technology solutions that help to improve the everyday lives of citizens.

Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new work, from MIT's Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces -- and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.