This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scenarios, by introducing a model based RL method to drive the autonomous vehicle in the Carla urban driving simulator. Although IL and model free RL methods have been proved to be capable of solving lots of challenging tasks, including playing video games, robots, and, in our prior work, urban driving, the low sample efficiency of such methods greatly limits their applications on actual autonomous driving. In this work, we developed a model based RL algorithm of guided policy search (GPS) for urban driving tasks. The algorithm iteratively learns a parameterized dynamic model to approximate the complex and interactive driving task, and optimizes the driving policy under the nonlinear approximate dynamic model. As a model based RL approach, when applied in urban autonomous driving, the GPS has the advantages of higher sample efficiency, better interpretability, and greater stability. We provide extensive experiments validating the effectiveness of the proposed method to learn robust driving policy for urban driving in Carla. We also compare the proposed method with other policy search and model free RL baselines, showing 100x better sample efficiency of the GPS based RL method, and also that the GPS based method can learn policies for harder tasks that the baseline methods can hardly learn.

The increasing ubiquity of low-cost wireless sensors in smart homes and buildings has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy concerns for third-party occupants, such as a hotel room guest who may be unaware of deployed clandestine sensors. Previous methods focused on specific modalities such as detecting cameras but do not provide a generalizable and comprehensive method to capture arbitrary sensors which may be "spying" on a user. In this work, we seek to determine whether one can walk in a room and detect any wireless sensor monitoring an individual. As such, we propose SnoopDog, a framework to not only detect wireless sensors that are actively monitoring a user, but also classify and localize each device. SnoopDog works by establishing causality between patterns in observable wireless traffic and a trusted sensor in the same space, e.g., an inertial measurement unit (IMU) that captures a user's movement. Once causality is established, SnoopDog performs packet inspection to inform the user about the monitoring device. Finally, SnoopDog localizes the clandestine device in a 2D plane using a novel trial-based localization technique. We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6% percent of the time, classify suspicious devices with 100% accuracy, and localize devices to a sufficiently reduced sub-space.

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ("Wells Fargo") and dates ("July 5, 2020") as well as certain productive constructions ("blow for blow", "day after day"). Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions. Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information. We empirically compare these two common strategies--parsing and tagging--for predicting flat MWEs. Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies. Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.

Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence and the cohesion of a text. The semantic coherence, cohesion, and other linguistic features (lexical diversity, lexical density) have been taken into account to form feature vectors for the training of a model-classifier. The training of the classifier has been performed on the set of English-language interviews. According to the retrieved results, the impact of each feature on the output of the model has been analyzed. The results obtained can indicate that the proposed method based on the graph of the consistency of phrases may be used in the different tasks of the detection of mental illness.

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this regard, the enormous size of ciphertexts involved in HE computations degrades computational efficiency. Near-memory Processing (NMP) and Computing-in-memory (CiM) - paradigms where computation is done within the memory boundaries - represent architectural solutions for reducing latency and energy associated with data transfers in data-intensive applications such as HE. This paper introduces CiM-HE, a Computing-in-memory (CiM) architecture that can support operations for the B/FV scheme, a somewhat homomorphic encryption scheme for general computation. CiM-HE hardware consists of customized peripherals such as sense amplifiers, adders, bit-shifters, and sequencing circuits. The peripherals are based on CMOS technology, and could support computations with memory cells of different technologies. Circuit-level simulations are used to evaluate our CiM-HE framework assuming a 6T-SRAM memory. We compare our CiM-HE implementation against (i) two optimized CPU HE implementations, and (ii) an FPGA-based HE accelerator implementation. When compared to a CPU solution, CiM-HE obtains speedups between 4.6x and 9.1x, and energy savings between 266.4x and 532.8x for homomorphic multiplications (the most expensive HE operation). Also, a set of four end-to-end tasks, i.e., mean, variance, linear regression, and inference are up to 1.1x, 7.7x, 7.1x, and 7.5x faster (and 301.1x, 404.6x, 532.3x, and 532.8x more energy efficient). Compared to CPU-based HE in a previous work, CiM-HE obtain 14.3x speed-up and >2600x energy savings. Finally, our design offers 2.2x speed-up with 88.1x energy savings compared to a state-of-the-art FPGA-based accelerator.

Competition is steep in games like football. The desire to win often trumps safety.

Soccer great Briana Scurry started playing soccer at 12 on an all boys team and in the goal — the "safest" position for a girl ...

For a long time after her injury, soccer great Briana Scurry "hid her hell." Now, she knows that that was not the right thing to do and she wants to teach others to become more open and understanding about concussion.

"The penalty kicks, the final goals in the Olympics, playing in front of the president, in front of 90,000 people ... that is what I was born to do ... and my brain is what I used to get myself there."

If you don't feel right after a concussion, talk to your parents, your coach, your doctor ... get a second, third, fourth opinion ... Do not accept that you will not get better.

"The Briana Scurry who could tune out 90,000 people during the World Cup and focus on a single ball and know I could keep it out of the goal ... that is who I want to be again."

As someone who had a phenomenal career in professional soccer and that had a career-ending head injury, Briana Scurry knows she can help other female — and male — athletes.

Soccer great Briana Scurry writes an open letter to young athletes about her love for soccer and the importance of taking concussions seriously.

Are sports organizations like FIFA taking concussions in sports seriously enough?

Personalization is one of the most effective marketing techniques to connect with customers online. While the exact methods are different for every business, adding personalized elements to landing pages is a proven method of driving conversions on your site. But why is it so successful? The simple answer is that personalization shows customers that you […]

The post How Personalized Landing Pages Can Make Your Site More Profitable appeared first on WebFX Blog.

When you bookmark pages on the web, it’s challenging to remember the name of the page. As you dive back into your bookmarks to find it, you see a small icon next to the page. You recognize the icon and realize it’s the website you viewed prior. This icon, known as a favicon, is small, […]

The post What is a Favicon? [+4 Tips for Creating an Impactful Favicon] appeared first on WebFX Blog.

WordPress powers 35% of all websites, which makes WordPress sites a go-to target for hackers. If you’re like most WordPress site owners, you’re probably asking the same question: Is my WordPress site secure? While you can’t guarantee site security, you can take several steps to improve and maximize your WordPress security. Keep reading to learn […]

The post Is My WordPress Site Secure? 13 Tips for Locking Down Your WordPress Site appeared first on WebFX Blog.

Did you know that breaking the Americans with Disabilities Act (ADA) can result in a six-figure fine? For every violation, companies can receive a $150,000 fine — and if you have a WordPress site, you could be liable. While WordPress aims to ensure website accessibility, it cannot guarantee it since every site owner customizes the […]

The post Is My WordPress Site ADA Compliant? 3+ Plugins for Finding Out! appeared first on WebFX Blog.

Subscribe to our YouTube channel for the latest in digital marketing! we know you’ll love this additional resource! (how to host a website)   Transcript: What is website hosting?  This is to make a point, I promise.  When you go to a party, there’s always a host. The host is usually the one who sets […]

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If you’re looking to grow your business online, it’s time to start setting up different types of website conversions to help your company succeed. Whether you’re looking to earn more email subscribers or sell more products, you can set conversion goals that grow your business. On this page, we’ll discuss what a conversion goal is, […]

The post Going Beyond Sales: 7 Types of Website Conversions to Optimize for on Your Website appeared first on WebFX Blog.

Did you know that for every second faster your website loads, you increase conversions by 7%? A fast loading website leads to longer dwell sessions, improved engagement, and increased conversions. When people can access information fast, they’re more likely to stay on your page. So now you’re probably wondering, “How fast should my website be?” […]

The post How Fast Should My Website Be? [+7 Tips for Speeding Up Your Site] appeared first on WebFX Blog.

By Evan H. DeLucia Courtesy of EOS Critics of biofuels like ethanol argue they are an unsustainable use of land. But with careful management, next-generation grass-based biofuels can net climate savings and improve their ecosystems. As the world seeks strategies … Continue reading →

By Energy.Gov Annual reports analyzing the wind energy industry released today by the Energy Department show continued rapid growth in wind power installations in 2015, demonstrating market resilience and underscoring the vitality of the U.S. wind energy market on a … Continue reading →

React Hooks, introduced in React 16.8, present us with a fundamentally new approach to coding. Some may think of them as a replacement for lifecycles or classes, however, that would be wrong. Like trying to translate a word from another language, sometimes you’re facing a completely new entity, which seems identical on the surface but is very different semantically and can’t be treated as equivalent. 

React not only changed the approach from OOP to Functional. The method of rendering has changed in principle. React is now fully built on functions instead of classes. And this has to be understood on a conceptual level. 

In the Alexa Skills for Node.JS ASK SDK development world, the Alexa Skills Kit (ASK) Command-Line Interface (CLI) is one of the most overlooked tools.

Boosting Developer Productivity

With proper use, one could really increase productivity when developing Alexa Skills. This is especially so if you are creating many Alexa Skills, either because you are in the learning process or you are just managing multiple Alexa Skills projects for yourself or your clients.

Node.js 14 is available now. We wanted to get more context and details about the state of Node, and why developers should care about Node.js 14. We talked with Bethany Griggs, Node.js Technical Steering Committee member and Open-source Engineer at IBM, to find out more. 

Bethany has been a Node Core Collaborator for over two years. She contributes to the open-source Node.js runtime and is a member of the Node.js Release Working Group where she is involved with auditing commits for the long-term support (LTS) release lines and the creation of releases. 

In the "Moving Away From AWS and Onto Heroku" article, I provided an introduction of the application I wanted to migrate from Amazon's popular AWS solution to Heroku.  Subsequently, the "Destination Heroku" article illustrated the establishment of a new Heroku account and focused on introducing a Java API (written in Spring Boot) connecting to a ClearDB instance within this new platform-as-a-service (PaaS) ecosystem.  My primary goal is to find a solution that allows my limited time to be focused on providing business solutions instead of getting up to speed with DevOps processes.

Quick Recap

As a TL;DR (too long; didn't read) to the original article, I built an Angular client and a Java API for the small business owned by my mother-in-law.  After a year of running the application on Elastic Beanstalk and S3, I wanted to see if there was a better solution that would allow me to focus more on writing features and enhancements and not have to worry about learning, understanding, and executing DevOps-like aspects inherent within the AWS ecosystem.

Introduction

Ngx-Bootstrap has released a package of open-source tools which are native Angular directives for Bootstrap 3 and 4. It contains all core components powered by Angular. In this article, we will learn about the Typehead component which is a cool feature of Ngx-bootstrap.

What Is Typeahead?

Typeahead — Also known as autocomplete or autosuggest is a language prediction tool that many search interfaces use to provide suggestions for users as they type in a textbox. This is a method for searching and filtering through text. It is also sometimes known as autocomplete, incremental search, search-as-you-type, and inline search.

Handling user input with forms is the cornerstone of many common applications.

Applications use forms to enable users to log in, to update a profile, to enter sensitive information, and to perform many other data-entry tasks

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This Pen uses: HTML, CSS, JavaScript, and

The mid-year slump hit hard this year. I’m rarely a prolific writer or blogger during the summer. Perhaps it’s the heat down here in south Alabama. It makes you want to sit under the shade of an old…

TL;DR: Brad Williams, John James Jacoby, and I will be publishing the 2nd edition of Professional WordPress Plugin Development this year. It is hard to believe, but it has been nine years since I was approached by Brad Williams…

The Cannabis Issue During normal times, I-90 Green House is like a destination resort for marijuana lovers.…

The Cannabis Issue It's been almost a month since the COVID-19 pandemic forced folks inside and made "social distancing" part of our daily lexicons.…

The Cannabis Issue Search online for information on CBD (cannabidiol), one of the main active components in marijuana and hemp plants, and you're likely to come across claims that it can help with everything from curing cancer to helping you sleep a little better.…

At these gatherings in northeast Washington, the jackboot of tyranny is always said to be descending, the hand of the federal government always inches away from stealing your guns, your land, your freedom to speak or to pray.…

Legal marijuana might be putting dealers out of work, but it's definitely not harming the job market in general.…

We've come a long way since the olden days before legalization, when basically the only product on the market was the flower you got from a dealer.…

Coronavirus Family Tree The University of Washington Virology lab, which is testing samples for coronavirus, tweeted last night.…

Since late last year, a new coronavirus, now dubbed COVID-19, has been sweeping the globe, sickening more than 114,000 with flu- and cold-like symptoms and killing more than 4,000 so far.…

By learning from a MERS outbreak in 2015, South Korea was prepared and acted swiftly to ramp up testing when the new coronavirus appeared there. Meanwhile, the U.S., plagued by delay and dysfunction, wasted its advantage. By Stephen Engelberg, Lisa Song and Lydia DePillis ProPublica…

There's one thing the Innovia Foundation can never say: That it hadn't been told.…

How the coronavirus outbreak has upended people's lives across the Inland Northwest The numbers don't lie.…