The line between humans and robots is blurred. You're on a mission either to hack into a corporation and steal its secret plans, or to advance those agendas on behalf of a powerful conglomerate. This is the plot of Android: Netrunner, a card game we've both played dozens of times during the pandemic, and neither of us is done getting vengeance on our opponent. After long days staring at our respective computer screens, we look forward to sitting down for a game where hackers install programs to access corporate servers. Even though the game went out of print in 2018, a fan group called Project NISEI has kept the enthusiasm alive by organizing tournaments and even designing and printing new cards that fans can add to their existing sets. A selling point of Netrunner is its inclusivity, which contrasts with many games that tend to feature American cities and characters that appear largely white and cis-gendered.

Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether a new fact should be accepted or not but are solely judged on the position of true facts in a likelihood ranking with other candidates. We argue that consideration of binary predictions is essential to reflect the actual KBC quality, and propose a novel evaluation paradigm, designed to provide more transparent model selection criteria for a realistic scenario. We construct the data set FB14k-QAQ where instead of single facts, we use KB queries, i.e., facts where one entity is replaced with a variable, and construct corresponding sets of entities that are correct answers. We randomly remove some of these correct answers from the data set, simulating the realistic scenario of real-world entities missing from a KB. This way, we can explicitly measure a model's ability to handle queries that have more correct answers in the real world than in the KB, including the special case of queries without any valid answer. The latter especially contrasts the ranking setting. We evaluate a number of state-of-the-art KB embeddings models on our new benchmark. The differences in relative performance between ranking-based and classification-based evaluation that we observe in our experiments confirm our hypothesis that good performance on the ranking task does not necessarily translate to good performance on the actual completion task. Our results motivate future work on KB embedding models with better prediction separability and, as a first step in that direction, we propose a simple variant of TransE that encourages thresholding and achieves a significant improvement in classification F1 score relative to the original TransE.

This paper unapologetically reflects on the critical role that Black feminism can and should play in abolishing algorithmic oppression. Positioning algorithmic oppression in the broader field of feminist science and technology studies, I draw upon feminist philosophical critiques of science and technology and discuss histories and continuities of scientific oppression against historically marginalized people. Moreover, I examine the concepts of invisibility and hypervisibility in oppressive technologies a l\'a the canonical double bind. Furthermore, I discuss what it means to call for diversity as a solution to algorithmic violence, and I critique dialectics of the fairness, accountability, and transparency community. I end by inviting you to envision and imagine the struggle to abolish algorithmic oppression by abolishing oppressive systems and shifting algorithmic development practices, including engaging our communities in scientific processes, centering marginalized communities in design, and consensual data and algorithmic practices.

The aircraft carrier Nimitz is finally going home. The Pentagon last month ordered the warship to remain in the Middle East because of Iranian threats against President Donald J. Trump and other American officials, just three days after announcing the ship was returning home as a signal to de-escalate rising tensions with Tehran. With those immediate tensions seeming to ease a bit, and President Biden looking to renew discussions with Iran on the 2015 nuclear accord that Mr. Trump withdrew from, three Defense Department officials said on Monday that the Nimitz and its 5,000-member crew were ordered on Sunday to return to the ship's home port of Bremerton, Wash., after a longer-than-usual 10-month deployment. The Pentagon for weeks had been engaged in a muscle-flexing strategy aimed at deterring Iran and its Shia proxies in Iraq from attacking American personnel in the Persian Gulf to avenge the death of Maj. General Suleimani, the commander of Iran's elite Quds Force of the Islamic Revolutionary Guards Corps, was killed in an American drone strike in January 2020.

Looking back, 2020 and the Covid-19 pandemic has been extremely difficult and disruptive to business and our personal lives. However, 2020 was not only deleterious – at least not with regard to technology. In many technology fields progress has accelerated significantly. Two of these areas are artificial intelligence (AI) and robotics, which will play a prominent role in 2021 and following years. Over the last few years AI has grown in importance in a wide variety of fields such as healthcare, bioscience, education, transport, marketing, finance, cybersecurity and many more.

We consider the problem of tensor estimation from noisy observations with possibly missing entries. A nonparametric approach to tensor completion is developed based on a new model which we coin as sign representable tensors. The model represents the signal tensor of interest using a series of structured sign tensors. Unlike earlier methods, the sign series representation effectively addresses both low- and high-rank signals, while encompassing many existing tensor models -- including CP models, Tucker models, single index models, several hypergraphon models -- as special cases. We show that the sign tensor series is theoretically characterized, and computationally estimable, via classification tasks with carefully-specified weights. Excess risk bounds, estimation error rates, and sample complexities are established. We demonstrate the outperformance of our approach over previous methods on two datasets, one on human brain connectivity networks and the other on topic data mining.

Colonies of naked mole rats develop dialects in their vocalisations that may help them distinguish between friends and foes. These dialects are influenced by each colony's queen, and become more varied if the queen dies. Naked mole rats (Heterocephalus glaber) are extremely vocal creatures that live in colonies in which only one queen reproduces. To see whether their vocalisations help maintain their social structure, Alison Barker at the Max Delbrück Center for Molecular Medicine in Germany and her colleagues recorded more than 36,000 greeting calls, from 166 naked mole rats in seven colonies raised in labs in Germany and South Africa. After identifying the acoustic features of these soft chirps, such as pitch, peak frequency and duration, the researchers used the calls to train a machine-learning algorithm.

We have entered an era of a pandemic that has shaken the world with major impact to medical systems, economics and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes any such modelling attempts unreliable. Hence we need to re-look at the situation with the latest data sources and most comprehensive forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. In this paper, prominent recurrent neural networks, in particular \textit{long short term memory} (LSTMs) networks, bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting the spread of COVID-infections among selected states in India. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak and provide two months ahead forecast that shows that cases will slowly decline. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas. We note that although we made some progress in forecasting, the challenges in modelling remain due to data and difficulty in capturing factors such as population density, travel logistics, and social aspects such culture and lifestyle.

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.