Accidents in inland waterways in developing countries are a regular phenomenon throughout the year causing deaths, injuries, monetary loss, and a significant amount of missing people. In consequence, a lot of families are losing their dear ones leading to much misery. The above context demands an intelligent, safe, and reliable water transport system for the developing countries. The concept of Intelligent Transport System (ITS) can be applied to develop such system; however, there are issues with ITS and Internet of Things (IoT) unlocks a new way of developing it. This paper proposes a model to transform the water transport system into an intelligent system based on IoT. IPv6 based machine-to-machine (M2M) protocol, 3G telecommunication technology, and IEEE 802.15.4 network standard play a significant role in this proposed IoT based system.

Human emotion recognition has become a need for more realistic and interactive machines and computer systems. The greatest challenge is the availability of high-performance algorithms to effectively manage individual differences and nonstationarities in physiological data streams, i.e., algorithms that self-customize to a user with no subject-specific calibration data. We describe an evolving Gaussian Fuzzy Classifier (eGFC), which is supported by an online semi-supervised learning algorithm to recognize emotion patterns from electroencephalogram (EEG) data streams. We extract features from the Fourier spectrum of EEG data. The data are provided by 28 individuals playing the games 'Train Sim World', 'Unravel', 'Slender The Arrival', and 'Goat Simulator' - a public dataset. Different emotions prevail, namely, boredom, calmness, horror and joy. We analyze the effect of individual electrodes, time window lengths, and frequency bands on the accuracy of user-independent eGFCs. We conclude that both brain hemispheres may assist classification, especially electrodes on the frontal (Af3-Af4), occipital (O1-O2), and temporal (T7-T8) areas. We observe that patterns may be eventually found in any frequency band; however, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, are the highest correlated with emotion classes. eGFC has shown to be effective for real-time learning of EEG data. It reaches a 72.2% accuracy using a variable rule base, 10-second windows, and 1.8ms/sample processing time in a highly-stochastic time-varying 4-class classification problem.

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training purely with synthetically generated theorems, without any human data aside from axioms. We use these theorems to train a neurally-guided saturationbased prover. Our neural prover outperforms the state-of-the-art E-prover on this synthetic data in both time and search steps, and shows significant transfer to the unseen human-written theorems from the TPTP library, where it solves 72% of first-order problems without equality. Most work applying machine learning to theorem proving takes the following approach: 1) pick a dataset of formalized mathematics, such as Mizar or Metamath, or the standard library of a major proof assistant such as HOL-Light or Coq; 2) split the dataset into train and test; 3) use imitation learning or reinforcement learning on the training set to learn a policy; and finally 4) evaluate the policy on the test set (Loos et al. (2017), Bansal et al. (2019), Yang & Deng (2019), Han et al. (2021), Polu & Sutskever (2020)). Such methods are fundamentally limited by the size of the training set, particularly when relying on deep neural networks (Kaplan et al., 2020). Unfortunately, unlike in computer vision and natural language processing, theorem proving datasets are comparatively tiny.

During the last years, many advances have been made in tasks like3D model retrieval, 3D model classification, and 3D model segmentation.The typical 3D representations such as point clouds, voxels, and poly-gon meshes are mostly suitable for rendering purposes, while their use forcognitive processes (retrieval, classification, segmentation) is limited dueto their high redundancy and complexity. We propose a deep learningarchitecture to handle 3D models as an input. We combine this architec-ture with other standard architectures like Convolutional Neural Networksand autoencoders for computing 3D model embeddings. Our goal is torepresent a 3D model as a vector with enough information to substitutethe 3D model for high-level tasks. Since this vector is a learned repre-sentation which tries to capture the relevant information of a 3D model,we show that the embedding representation conveys semantic informationthat helps to deal with the similarity assessment of 3D objects. Our ex-periments show the benefit of computing the embeddings of a 3D modeldata set and use them for effective 3D Model Retrieval.

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.

Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling such intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups based on race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at github.com/bhavyaghai/WordBias.

SCI COMMUN### COVID-19 Johnson & Johnson (J&J) last week became the third COVID-19 vaccinemaker to receive emergency use authorization for its product from the U.S. Food and Drug Administration. In contrast to the two-dose vaccines from Moderna and Pfizer authorized earlier, the J&J vaccine—a harmless virus delivering the gene for the spike protein from SARS-CoV-2—proved safe and effective with a single dose. The company intends to deliver 20 million doses to the United States this month, 80 million more by the end of June, and more than 1 billion doses worldwide this year. A placebo-controlled trial that took place in eight countries and involved more than 43,000 participants found that the single shot had 66% efficacy against moderate to severe COVID-19 after 28 days and 85% protection against severe disease. This is below the approximately 95% efficacy against mild disease achieved by the Pfizer and Moderna vaccines, which produce the spike protein using messenger RNA (mRNA)—but the J&J trial included locations in South Africa and Brazil where SARS-CoV-2 variants that may escape vaccine-induced antibodies are now common. (The mRNA vaccine results came before their spread.) No one who received the J&J vaccine in any country was hospitalized or died from COVID-19. The White House also brokered a deal with Merck, a major vaccine producer that dropped its own COVID-19 candidates because of poor performance, to help make the J&J product. ### Archaeology A detailed excavation in what was once a Roman port city has helped archaeologists identify what may be the oldest known pet cemetery. The remains of nearly 600 cats and dogs had been laid in prepared pits and covered with pieces of pottery and textiles, and some wore collars and other adornments. Researchers discovered the graveyard in 2011 outside the ancient city of Berenice, which today lies in Egypt. Features of some skeletons indicated the animals had lived with debilitating injuries and illnesses and survived into old age, indicating the animals were cared for, the researchers reported recently in World Archaeology . ### Art and science “I'm the first author, you're just et al. ,” raps this year's winner of Science 's annual “Dance Your Ph.D.,” a contest that challenges scientists to explain their research through dance. The first place video by Jakub Kubečka, a doctoral student at the University of Helsinki, features an original rap song and choreography, performed by him and two friends (above), explaining the search for atmospheric molecular clusters—groups of atoms that stick together and encourage water vapor to condense into clouds. Kubečka beat out 39 competitors for the $2000 top prize, sponsored by the artificial intelligence firm Primer. The winning entry is at . ### COVID-19 The U.S. National Institutes of Health (NIH) last week announced a multipronged research effort to better understand and treat Long COVID, in which people suffer lingering effects after infection by the pandemic coronavirus. Symptoms include lung problems, heart abnormalities, and enduring fatigue. In December 2020, Congress gave NIH $1.15 billion over 4 years to study the perplexing condition. The agency is inviting applications for research on its natural history, prevalence, and underlying biology and plans an expansive biorepository for samples from volunteers. Last week, the World Health Organization released a policy document estimating 10% of patients remain unwell 12 weeks after being infected. Explanations for the lasting effects have been elusive ( Science , 7 August 2020, p. [614][1]). ### Climate change Countries are drastically lagging in the fossil fuel cuts needed to reach the goals of the Paris climate agreement, a new analysis suggests. Globally, emissions were up by an average of 0.21 billion tons of carbon dioxide (CO2) per year from 2016 to 2019 compared with 2011–15, the Global Carbon Project reported this week in Nature Climate Change . Although 64 countries—most of them wealthy ones that have contributed the most to climate change—cut their CO2 emissions by a collective 0.16 billion tons per year during this time, their reductions must increase 10-fold, to some 1 billion tons annually, to meet the Paris goal of limiting global warming to 2°C. Although the pandemic caused a 7% drop in emissions in 2020, the report says, evidence from previous economic crises suggests emissions will rebound to previous levels unless recovery plans aggressively push decarbonization. ### Immigration U.S. President Joe Biden last week ended a policy, imposed last year by then-President Donald Trump, that had barred most noncitizens not already in the United States from seeking permanent residency and work permits, or green cards. Trump had said issuing new green cards didn't make sense given unemployment caused by the COVID-19 pandemic. But industry groups had challenged the policy, in part because they said it prevented companies from hiring needed scientists and skilled technical workers. In revoking the ban, Biden said it had harmed U.S. businesses “that utilize talent from around the world.” A Trump ban on temporary work permits remains in place but is set to expire on 31 March. ### Climate policy U.S. President Joe Biden's administration last week raised the government's benchmark for the “social cost of carbon,” the estimate it uses in cost-benefit analyses of regulations and other policies to represent the burden that global warming places on present and future generations. The figure will rise to $51 per ton on an interim basis; former President Donald Trump's administration had set it as low as $1. The revised standard restores the level set under former President Barack Obama, adjusted for inflation. The Biden administration may further increase the figure in an update due in January 2022 to reflect increased damages from heat waves and other disasters made worse by global warming. ### Publishing A study of more than 5000 biomedical journals found a pattern of apparent favoritism: In 206 journals, a single author was responsible for between 11% and 40% of the papers published between 2015 and 2019. Of 100 of these “nepotistic” journals given closer scrutiny, the prolific author was the editor-in-chief for about one-quarter and on the editorial board for more than 60%. Prolific authors also enjoyed faster peer reviews, according to a preprint of the study posted last month on the bioRxiv server. A research team did the analysis after scrutinizing publications by microbiologist Didier Raoult of Aix-Marseilles University, who has promoted hydroxychloroquine as a COVID-19 treatment, although most other studies have found no evidence of benefit. Raoult, who now faces disciplinary action by a French medical regulator, appears as an author on one-third of the 728 papers at the journal New Microbes and New Infections , where some of his collaborators serve as editors. ### Science and art An artificial intelligence (AI) program for the first time has written a play, which was staged by actors in Prague's Švanda Theater and premiered online last week. The script, depicting a robot's journey trying to understand humans, was generated by a widely available AI system called GPT-2. Researchers at Charles University helped it start to write the play by feeding it two sentences of dialogue about human experiences, and the software generated more, using related information drawn from the internet. Dramatist David Košt'ák, who tweaked about 10% of the resulting script to ensure it followed a coherent storyline, called its style “abstract.” But AI: When a robot writes a play showcases what the evolving technology can now do, specialists say. Judge the 60-minute play for yourself at . ### Conservation The population of monarch butterflies overwintering in Mexico showed another big drop this year. Researchers counted 2.1 hectares of occupied habitat, down 26% from last year and more than 80% from 2 decades ago, the Center for Biological Diversity said. Six hectares is the minimum considered necessary to avoid a risk of extinction. DINO TRACKS IN PERIL Ongoing mining threatens to destroy China's largest site of dinosaur tracks, researchers reported online on 27 February in Geoscience Frontiers . In 1994, the first of the tracks, 145 million to 120 million years old, were uncovered in a copper mine in China's southwestern Sichuan province. Paleontologists have identified 1928 individual footprints from dozens of individuals, representing ornithopods, theropods, sauropods, and pterosaurs. By 2012, mining had led one of the track-bearing rock faces to collapse, before it was fully studied. NEW MINE HELD UP The Biden administration has delayed a huge Arizona copper mine opposed by archaeologists and Native tribes, who say it will destroy cultural treasures. In 2014 Congress approved giving 970 hectares of federal land at Oak Flat to a mining firm. But this week officials said they want to review an environmental study needed for the transfer. Mine opponents have asked Congress and the courts to kill the project. A WIN AGAINST MALARIA El Salvador last week became the first country in Central America to be certified free of malaria by the World Health Organization. The country became eligible after recording no home-grown cases of the mosquito-borne disease since 2017. Globally, 38 countries and territories have reached this milestone. BIG CANCER FUND A new foundation will provide $250 million for cancer research, one of the largest such gifts ever. Break Through Cancer was financed by a Richmond, Virginia, businessperson whose son died of cancer in 2020. The funding will support research teams drawn from five prominent U.S. university cancer centers that will study cancer types that are difficult to treat and have high mortality rates, including pancreatic and ovarian cancer, glioblastomas, and acute myelogenous leukemia. NO PLACE LIKE HOME Americans value space research aimed at protecting Earth over sending astronauts to other bodies, a survey by Morning Consult says. Sixty-three percent of respondents called monitoring Earth's climate a top or important priority. Just 33% voiced that level of backing for launching astronauts to Mars or the Moon. ### Should peer reviewers be paid? Reviewing journal articles can seem a thankless task. Scientists do the work for free, even as as journals publish ever more papers and some publishers make sizable profits. Even before the COVID-19 pandemic led to a blizzard of submissions, journal editors were reporting that “reviewer fatigue” was making it harder to find volunteers. At the Researcher to Reader conference on scholarly publishing last week, two teams debated a provocative question: Should peer reviewers be paid? Here are some of their arguments. (See a fuller version at .) YES: “There is no downward pressure on the endless use of academic labor. And the easiest way to exert that pressure is to value the task not [only] with recognition, but with the traditional way to support skilled labor in every other industry, which is money.” James Heathers, a former research scientist, now chief scientist at a technology startup NO: “A 2018 survey found that only 17% of respondents selected cash or in-kind payment as something that would make them more likely to accept review requests.” (Nearly half said more explicit recognition of reviewing work from their universities or employers would inspire them to do it.) Alison Mudditt, CEO of PLOS, a nonprofit publisher of open-access articles NO: It could cost $3960 per accepted paper to cover the cost of reviewing, if each reviewer was paid $450, each manuscript received 2.2 reviews, and the journal accepted 25% of submissions. “Surely that money would better spent on the research itself and on solving our most pressing global challenges.” Tim Vines, a publishing consultant YES: “What might very well happen is fewer papers get submitted, because the costs go up. … A contract provides much needed certainty around the time frame, the quality, and the predictability of the review received.” Brad Fenwick, senior vice president at Taylor & Francis, a for-profit publisher NO: “It's completely unrealistic to expect that anybody is going to have either the time or the expertise or the scale to be able to manage and monitor hundreds of thousands of additional new contracts across the publishing system. Just not gonna happen.” A.M. [1]: http://www.sciencemag.org/content/369/6504/614

Deciphering what flavor notes are present in a given coffee is more art than science. Tasters spend hours upon hours training their palates, translating the sensory information through the lens of past (highly subjective) flavor experiences to arrive at descriptors that best align with the original sensory input. But one startup is looking to take a more scientific approach to identifying flavors in coffee. The company is called Demetria and they have created an app to "detect a specific and high value sensory ("taste") profile of green coffee." In a press release announcing the company, Demetria touts itself as "the first AI-powered taste and quality intelligence [software as a service] startup for the coffee supply chain."

Tensor decomposition is a fundamentally challenging problem. Even the simplest case of tensor decomposition, the rank-1 approximation in terms of the Least Squares (LS) error, is known to be NP-hard. Here, we show that, if we consider the KL divergence instead of the LS error, we can analytically derive a closed form solution for the rank-1 tensor that minimizes the KL divergence from a given positive tensor. Our key insight is to treat a positive tensor as a probability distribution and formulate the process of rank-1 approximation as a projection onto the set of rank-1 tensors. This enables us to solve rank-1 approximation by convex optimization. We empirically demonstrate that our algorithm is an order of magnitude faster than the existing rank-1 approximation methods and gives better approximation of given tensors, which supports our theoretical finding.

The recently proposed EMDE (Efficient Manifold Density Estimator) model achieves state of-the-art results in session-based recommendation. In this work we explore its application to Booking Data Challenge competition. The aim of the challenge is to make the best recommendation for the next destination of a user trip, based on dataset with millions of real anonymized accommodation reservations. We achieve 2nd place in this competition. First, we use Cleora - our graph embedding method - to represent cities as a directed graph and learn their vector representation. Next, we apply EMDE to predict the next user destination based on previously visited cities and some features associated with each trip. We release the source code at: https://github.com/Synerise/booking-challenge.