Robots in our daily surroundings are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. The theory of f-formation can be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining all the possible concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain.

Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries; experts suggest that large-scale coordination is necessary to alleviate demand. Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. While measuring actual donation rates remains a challenge, we measure donor action (e.g., making a donation appointment) as a proxy for actual donation. We develop automated policies for matching donors with donation opportunities, based on an online matching model. We provide theoretical guarantees for these policies, both regarding the number of expected donations and the equitable treatment of blood recipients. In simulations, a simple matching strategy increases the number of donations by 5-10%; a pilot experiment with real donors shows a 5% relative increase in donor action rate (from 3.7% to 3.9%). When scaled to the global Blood Donation tool user base, this corresponds to an increase of around one hundred thousand users taking action toward donation. Further, observing donor action on a social network can shed light onto donor behavior and response to incentives. Our initial findings align with several observations made in the medical and social science literature regarding donor behavior.

This paper presents the Spanish RoBERTa-base and RoBERTa-large models, as well as the corresponding performance evaluations. Both models were pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain from 2009 to 2019. We extended the current evaluation datasets with an extractive Question Answering dataset and our models outperform the existing Spanish models across tasks and settings.

AI can spot patterns in brain scans that even the most experienced neurologists can't see Scientists are testing an artificial intelligence system they believe could lead to a diagnosis of dementia following a brain scan. It can also predict whether the condition will remain stable for many years, worsen gradually, or whether the patient will require immediate treatment. Currently, a number of tests and CT scans are needed to diagnose dementia. Researchers involved in the study say that early diagnosis with the system they have developed can significantly improve patients' prognosis. "If we intervene earlier, treatments can act earlier and delay the progression of the disease, and at the same time, if we intervene earlier, can prevent further damage." of the Alan Turing Institute of Artificial Intelligence and Data Science.

Talkdesk, a provider of cloud-based contact center software, announced $230 million in new Series D funding that more than triples the company's valuation to $10 billion, Talkdesk founder CEO Tiago Paiva confirmed to TechCrunch. New investors Whale Rock Capital Management, TI Platform Management and Alpha Square Group came on board for this round and were joined by existing investors Amity Ventures, Franklin Templeton, Top Tier Capital Partners, Viking Global Investors and Willoughby Capital. Talkdesk uses artificial intelligence and machine learning to improve customer service for midmarket and enterprise businesses. It counts over 1,800 companies as customers, including IBM, Acxiom, Trivago and Fujitsu. "The global pandemic was a big part of how customers interact and how we interacted with our customers, all working from home," Paiva said.

All the sessions from Transform 2021 are available on-demand now. Talkdesk, which provides an enterprise contact center platform, today announced that it raised $230 million at a post-money valuation of $10 billion. The round, which came from Whale Rock Capital Management, TI Platform Management, Alpha Square Group, Amity Ventures, Franklin Templeton, Top Tier Capital Partners, Viking Global Investors, and Willoughby Capital, brings the company's total raised to $498 million to date. Over the past several years, businesses have increasingly turned to cloud-based contact centers to address budding customer service challenges. The pandemic accelerated that move -- service conveniences were put in place out of necessity, which gave customers more options for interacting with companies. For example, 78% of contact centers in the U.S. now intend to deploy AI in the next 3 years, according to Canam Research.

Credit scoring models, which are among the most potent risk management tools that banks and financial institutes rely on, have been a popular subject for research in the past few decades. Accordingly, many approaches have been developed to address the challenges in classifying loan applicants and improve and facilitate decision-making. The imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring datasets, pose difficulties in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. In this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC) that mainly leverages the superior performance of the Supervised Autoencoder, which learns low-dimensional embeddings of the input data exclusively with regards to the ultimate classification task of credit scoring, based on the principles of multi-task learning. BSAC also addresses the data imbalance problem by employing a variant of the Bagging process based on the undersampling of the majority class. The obtained results from our experiments on the benchmark and real-life credit scoring datasets illustrate the robustness and effectiveness of the Bagging Supervised Autoencoder Classifier in the classification of loan applicants that can be regarded as a positive development in credit scoring models.

Deep learning algorithms mine knowledge from the training data and thus would likely inherit the dataset's bias information. As a result, the obtained model would generalize poorly and even mislead the decision process in real-life applications. We propose to remove the bias information misused by the target task with a cross-sample adversarial debiasing (CSAD) method. CSAD explicitly extracts target and bias features disentangled from the latent representation generated by a feature extractor and then learns to discover and remove the correlation between the target and bias features. The correlation measurement plays a critical role in adversarial debiasing and is conducted by a cross-sample neural mutual information estimator. Moreover, we propose joint content and local structural representation learning to boost mutual information estimation for better performance. We conduct thorough experiments on publicly available datasets to validate the advantages of the proposed method over state-of-the-art approaches.

We present a unified approach to (both finite and unrestricted) worst-case optimal entailment of (unions of) conjunctive queries (U)CQs in the wide class of "locally-forward" description logics. The main technique that we employ is a generalisation of Lutz's spoiler technique, originally developed for CQ entailment in ALCHQ. Our result closes numerous gaps present in the literature, most notably implying ExpTime-completeness of (U)CQ-querying for any superlogic of ALC contained in ALCHbregQ, and, as we believe, is abstract enough to be employed as a black-box in many new scenarios.

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 and 0.051.