Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on $mathcal{S}$-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.

The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT.

Each translation presented tremendous theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose.

We believe similar library translations will be an essential part of any future system interoperability solution and our experiences will prove valuable to others undertaking such efforts.

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN

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.

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.

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.

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.

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

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 ...

"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 talks about how all her successes started with her mind and her ability to overcome obstacles. After her injury, she felt lost, broken.

After several weeks of not playing because of a concussion and then failing  several baseline tests, Briana Scurry became very worried.

"I knew I was in trouble ... I didn't know how much trouble," says retired soccer star Briana Scurry.

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

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.

If you’re not already thinking about 2020 web design, the time is now. Already, web design trends for 2020 have started to emerge, and if you want to stay on-trend and engage site visitors, it’s crucial to pay attention. But what is the future of web design in 2020? Will everything change? Well — not […]

The post Pay Attention to These Web Design Trends for 2020 [7+ Trends] 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.

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.

The nail biting. The endless coffee. The sleepless nights. You can’t keep it up, but you need to know: Is my site hacked? Good news, you don’t have to stay up all night, grind down your nails, or consume all the coffee in the building to find out if your website has been hacked. You […]

The post Is My Site Hacked? 6 Ways to Find If Your Site’s Been Hacked appeared first on WebFX Blog.

Web design is responsible for nearly 95% of a visitor’s first impression of your business. That’s why it’s more important than ever to incorporate modern web design into your marketing strategy. But what modern web design trends are on the horizon for 2020 — and how can you use them to freshen up your site? […]

The post 10 Modern Web Design Trends for 2020 appeared first on WebFX Blog.

Need some inspiration to build a high converting website? Websites that convert persuade visitors to become customers. These websites drive more revenue, so if you want to increase your site’s revenue, use these examples of websites that convert as inspiration! We’ll go over what makes for the best converting websites and five examples of websites […]

The post 5 Magnificent Examples of Websites That Convert Visitors into Customers appeared first on WebFX Blog.

By Jon Queally Common Dreams Greenpeace says world leaders must not let the fossil fuel industry stand in the way of the necessary—and attainable—transition to a clean and safe energy future With scientists and experts from around the world telling … Continue reading →

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 Jeff Mulhollem Penn State News The first harvest of 34 acres of fast-growing shrub willow from a Penn State demonstration field this winter is a milestone in developing a sustainable biomass supply for renewable energy and bio-based economic development, … Continue reading →

By Andrea Germanos Common Dreams On the heels of clean fuel milestones in Germany and Portugal , a new report finds that the renewable energy industry employed over 8.1 million people worldwide in 2015. According to the International Renewable Energy … Continue reading →

By The Office of Energy Efficiency & Renewable Energy Within 25 years, the United States could produce enough biomass to support a bioeconomy, including renewable aquatic and terrestrial biomass resources that could be used for energy and to develop products … 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. 

.NET is indeed an important application development platform, as it's secure, robust, and quite easy to learn and implement. Developers are widely using the .NET framework to build web applications and even modernize legacy programming based applications into .NET-based ones. .NET developers also use many third-party tools to carry out development. These tools have proven to provide the best support for development.

Here are some of the top useful tools being used by many.NET development teams, .NET developers, individual .NET programmers, etc.

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.

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Playing with non -intersecting circles and gradients. Sometimes gives an impression of relief. When clicking, mouse horizontal position chooses hue and vertical position chooses with or without nested circles.

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Horizontal and Vertical scrolling with faded out content. **Please note:** I have not cross browser tested this, however this method leverages `background-attachment: local`, currently usable in everything except Android Browser & Opera Mini according to <a href="https://caniuse.com/#feat=background-attachment" target="_blank">caniuse.com</a>, meaning there is great support across devices and many Android devices use Google Chrome for Android rather than the OS browser. **Additional note:** There is a bug when previewing this pen on mobile, due to loading the example within an `iframe`. The shadow rgba values are read as a non transparent, this does not happen when previewing locally not in an iframe :-)

This Pen uses: HTML, SCSS, JavaScript, and

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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 So many of us are stuck inside right now, and that lack of socializing means we're all probably going a little bit stir crazy.…

Summer Camps 2020 Boy Scouts Camp Easton Spend a week on the shores of Lake Coeur d'Alene at camp, which offers activities like swimming, water-skiing, boating, sailing, kayaking, paddleboarding, hiking and more.…

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.…

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.…