Atima Lui was in primary school when she first learned that "nude" is not universal. Now 30, she still recalls playing with a white friend's makeup and struggling to find colours that complemented her rich skin tone. "I would try to put [her makeup] on and it would just make me look like a clown," says Lui, who is of Sudanese and African-American descent. "I think back to growing up and how my mother barely wore makeup. Now I know it's because makeup just wasn't made for her."

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In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators. For filters of full length, we propose a stochastic gradient descent algorithm on a submanifold of the Stiefel manifold to train cPNNs. In case of filters with limited length, we design algorithms for minimizing functionals that approximate the orthogonality constraints imposed on the operators by penalizing the least squares distance to the identity operator. Then, we investigate how scaled cPNNs with a prescribed Lipschitz constant can be used for denoising signals and images, where the achieved quality depends on the Lipschitz constant. Finally, we apply cPNN based denoisers within a Plug-and-Play (PnP) framework and provide convergence results for the corresponding PnP forward-backward splitting algorithm based on an oracle construction.

Evaluating the banks' performance has always been of interest due to their crucial role in the economic development of each country. Data envelopment analysis (DEA) has been widely used for measuring the performance of bank branches. In the conventional DEA approach, decision making units (DMUs) are regarded as black boxes that transform sets of inputs into sets of outputs without considering the internal interactions taking place within each DMU. Two-stage DEA models are designed to overcome this shortfall. Thus, this paper presented a new two-stage DEA model based on a modification on Enhanced Russell Model. On the other hand, in many situations, such as in a manufacturing system, a production process or a service system, inputs, intermediates and outputs can be given as a fuzzy variable. The main aim of this paper is to build and present a new fuzzy two-stage DEA model for measuring the efficiency of 15 branches of Melli bank in Hamedan province.

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82,42%, 96,78% and 54,76% for the three word embedding sets respectively.

Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symbolic operations and informal inference based on language understanding. In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models. Specifically, a verbalization with program execution model is proposed to accumulate evidences that are embedded in operations over the tables. Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision. To support the above framework, we propose a program selection module optimized with a new training strategy based on margin loss, to produce more accurate programs, which is shown to be effective in enhancing the final verification results. Experimental results show that the proposed framework achieves the new state-of-the-art performance, a 74.4% accuracy, on the benchmark dataset TABFACT.

With increased speed, enhanced force and lower cost, the application of artificial intelligence (AI) on weapons will define the next generation of warfare, Gary Butler, founder of Starkville-based tech company Camgian Microsystems, told Starkville Rotary Club members Monday afternoon. Butler, a Mississippi native, has been working on advanced technology for roughly 20 years, with a focus on sensor systems and AI-based technologies, according to his LinkedIn page. Over the years, he has worked on system development with the U.S. military and the Defense Advanced Research Projects Agency (DARPA). Camgian, which he founded in 2006, has provided research and development service to several government and financial agencies. With years of experience researching AI technology and its military use, Butler said the application of the technology in weaponry seems the natural step.

When Black Lives Matter protests were raging following the death of George Floyd, the publishers of the tabletop role-playing game Dungeons & Dragons, pledged to take concrete steps to make their games more diverse. Wizards of the Coast promised to "share what we've been doing, and what we plan to do in the future to address legacy D&D content that does not reflect who we are today". In addition, it also pulled several racist cards from the card game Magic: The Gathering, such as Invoke Prejudice, Jihad and Pradesh Gypsies. Is it a coincidence that D&D's dishonourable, dark-skinned elves come from a matriarchal society, or that its savage orcs bear uncanny resemblance to a traditionally white, western conceptualisation of barbaric peoples from the "uncivilised" world? Although fantasy affords us every freedom to imagine new worlds and cultures, for the last 200-odd years, humans have mostly managed derivative facsimiles of our own.

Nedjma Ousidhoum is a PhD candidate at Hong Kong University of Science and Technology. She also serves as an AIhub ambassador and has written a number of articles for us. In this interview we talk about her PhD, her research into hate speech detection, and the importance of considering AI ethics. I've been in Hong Kong for more than six years now. I came for a post-graduate internship then I stayed for a PhD. I wanted to experience living and working in Asia.

Video games have become extraordinarily adept at simulating geography, from Assassin's Creed's detailed, architecturally accurate takes on ancient Egypt or 18th-century Paris to Microsoft Flight Simulator's virtual simulacrum of the Earth's surface. But they are still no good at simulating people, and their cities are populated with reactive automatons who forget you tried to run them over two seconds ago. This makes Watch Dogs Legion's attempt to simulate the entire population of a futuristic, technocratic London one of the most ambitious things a game has tried in years. Walk from Camden to Nine Elms and every person you see has a name, a cluster of attributes (gambler, fashion expert, paramedic, low mobility) and a custom-generated voice and appearance. You can recruit any of them to your hacker resistance movement and step into their shoes.