Behavioral research can provide important insights for SE practices. But in performing it, many studies of SE are committing a normative fallacy - they misappropriate normative and prescriptive theories for descriptive purposes. The evidence from reviews of empirical studies of decision making in SE suggests that the normative fallacy may is common. This article draws on cognitive psychology and behavioral economics to explains this fallacy. Because data collection is framed by narrow and empirically invalid theories, flawed assumptions baked into those theories lead to misleading interpretations of observed behaviors and ultimately, to invalid conclusions and flawed recommendations. Researchers should be careful not to rely solely on engineering methods to explain what people do when they do engineering. Instead, insist that descriptive research be based on validated descriptive theories, listen carefully to skilled practitioners, and only rely on validated findings to prescribe what they should do.

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.

We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality !L*, which has a limited edition of the contraction and permutation rules. The categorical part of the semantics is a monoidal biclosed category with a coalgebra modality, very similar to the structure of a Differential Category. We instantiate this category to finite dimensional vector spaces and linear maps via "quantisation" functors and work with three concrete interpretations of the coalgebra modality. We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !L*: the derivation of a phrase with a parasitic gap. The effectiveness of the concrete interpretations are evaluated via a disambiguation task, on an extension of a sentence disambiguation dataset to parasitic gap phrase one, using BERT, Word2Vec, and FastText vectors and Relational tensors.

An approach is given for solving large linear systems that combines Krylov methods with use of two different grid levels. Eigenvectors are computed on the coarse grid and used to deflate eigenvalues on the fine grid. GMRES-type methods are first used on both the coarse and fine grids. Then another approach is given that has a restarted BiCGStab (or IDR) method on the fine grid. While BiCGStab is generally considered to be a non-restarted method, it works well in this context with deflating and restarting. Tests show this new approach can be very efficient for difficult linear equations problems.

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.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

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.

Motivated by the current demand of clinical governance, surgical simulation is now a well-established modality for basic skills training and assessment. The practical deployment of the technique is a multi-disciplinary venture encompassing areas in engineering, medicine and psychology. This paper provides an overview of the key topics involved in surgical simulation and associated technical challenges. The paper discusses the clinical motivation for surgical simulation, the use of virtual environments for surgical training, model acquisition and simplification, deformable models, collision detection, tissue property measurement, haptic rendering and image synthesis. Additional topics include surgical skill training and assessment metrics as well as challenges facing the incorporation of surgical simulation into medical education curricula.

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.

Despite the improvements in helmet technology, helmets may prevent skull fractures, but they do not prevent concussions.

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

The decisions coaches make on the sidelines about returning a concussed player to the game or not can be a "game changer" for that athlete's life.

Research is showing that the accumulation of sub-concussive hits in sports like football can be just as damaging as one or two major concussions.

For many competitive high school football players like Garrett Harper, the intensity of this contact sport has its price.

The issues of sports-related concussions and chronic traumatic encephalopathy were intensified when the brain of a deceased 21-year-old football player was examined.

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.

After she was hit, retired soccer star Briana Scurry felt off balance, sensitive to light and sound,and felt intense pain in her head and neck.

"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."

Retired Soccer Star Briana Scurry describes how the computerized baseline test works and how it is used for athletes who have sustained a concussion.

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.

Bilateral occipital nerve release surgery was the first, significant step to relieving Scurry's debilitating post-concussive headaches.

One out of two girls will sustain a concussion playing soccer, but most will recover and return to play with ease. Nevertheless, awareness and education are key to keeping players safe.

Retired soccer star Briana Scurry talks about finally finding hope and help after almost three years of being told she wouldn't get any better.

Why do some parents and coaches think it's okay to let 9-year-old kids get hit in the head over and over in football practices and games?

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?

A new study suggests that vulnerability to CTE is not limited to professional athletes.

Despite multiple concussions, a high school freshman continues to play football. Will family tradition outweigh the risks?

What happens when the drive to play outweighs the potential risk of injury? Some high school athletes are finding ways around the precautions coaching and medical staff take to ensure their safety.

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.

Your website serves several important purposes for your company — attracting customers, generating leads, and making sales, just to name a few. And as your home on the Internet, it also needs to explain who you are to the world and why they should choose you over your competitors.   However, creating an “About Us” […]

The post 7 Examples of Great “About Us” Pages appeared first on WebFX Blog.

Traffic isn’t great, online sales are even worse, and let’s not talk about the lack of phone calls. Everyone, including you, is wondering the same thing — is your website a failure? Not yet, and not if you have anything to say about it. While a failing website can seem like a problem without a […]

The post Is Your Website a Failure? 3 Reasons Sites Fail (And How to Save Yours) appeared first on WebFX Blog.

Does your website feature design straight out of the ’90s and functionality from the stone age? If so, it’s time for an upgrade — and WebFX can help. When it comes to website redesign checklists, we’re at the top of our game, and we know how to get things done. But where do you start […]

The post Website Redesign Checklist + 7 Handy Website Redesign Tips 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.

In this article, we'll explore 10 quick and easy ways to improve your site's accessibility.

The post 10 Helpful Tips for How to Make Your Website More Accessible 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.

Are you looking to generate more leads and revenue with your website? If so, it’s time to consider web design for lead generation to help you create a website that caters to your audience and encourages them to become leads for your business.  On this page, we’ll provide you with five lead generation website design […]

The post 5 Lead Generation Website Design Best Practices 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.

Dozens of people find your business when looking for a type of product but aren’t sure which product fits their needs best. With a well-designed and organized category page, you’ll help people browse products easier and find what they want. To help you get inspired, let’s take a look at some excellent category page design […]

The post Category Page Design Examples: 6 Category Page Inspirations appeared first on WebFX Blog.

Are you looking for a content management system (CMS) that will help you create the digital content you need? With so many options on the market, it’s challenging to know which one is the best CMS software for your business. On this page, we’ll take a look at the six best CMS’s for website development […]

The post 6 Best CMS Software for Website Development & SMBs appeared first on WebFX Blog.

Subscribe to our YouTube channel for the latest in digital marketing! Transcript: Your website isn’t set in stone, so you shouldn’t treat it like it is.  Technology and the internet change quickly, and often. You should update your website regularly to keep up with the times. Having an up-to-date and optimized site creates a great […]

The post 5 Simple Tips for How to Update Content on Your Website appeared first on WebFX Blog.

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.