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first_imgA VICTIM of paedophile priest Father Eugene Greene today spoke out about a huge cloud of suspicion over west Donegal – the public cash collection for the jailed cleric.As much as €50,000 was collected by parishoners in Annagry and the surrounding areas when Greene was released in 2008.Today one victim told donegaldaily.com: “It said a lot about some people that they went around collecting money for a man who actually pleaded guilty to raping me and many others. “I remember when I heard about this for the first time; I thought it was a sick joke. But then I was told that it had happened and I felt physically sick.“I don’t understand the mentality of these people….I don’t understand how anyone could hand over money so that an evil b****** like this could live in comfort after getting out of jail.”Greene has been in the headlines this week as some of his victims spoke out about the attacks on them.The BBC documentary has also put huge pressure on Cardinal Sean Brady over his handling of sex abuse claims against pervert priest Father Brendan Smyth. It’s understood Cardinal Brady could offer his resignation this weekend.The Greene cash collection however still hurts many of his victims.“It was like being raped all over again,” said the victim who spoke to donegaldaily.com.“The same people who collected money for Greene shunned his victims. We got not a cent in money, though we never wanted that, and we got not a single breath of comfort or apology. It is an eternal shame on these people.” ‘CASH COLLECTION FOR PAEDOPHILE PRIEST WAS LIKE BEING RAPED ALL OVER AGAIN’ – VICTIM was last modified: May 3rd, 2012 by BrendaShare this:Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Telegram (Opens in new window)Click to share on WhatsApp (Opens in new window)Click to share on Skype (Opens in new window)Click to print (Opens in new window) Tags:’CASH COLLECTION FOR PAEDOPHILE PRIEST WAS LIKE BEING RAPED ALL OVER AGAIN’ – VICTIMEUGENE GREENElast_img read more

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Survey NFL Players More Concerned With Leg Injuries Than

A survey of current NFL players done by USA Today finds that players are more concerned about leg injuries than they are about head injuries.The survey of 290 players who were on rosters in December indicated that 46 percent were most concerned about a knee or leg injury compared to 24 percent who were most worried by the prospect of head or neck injuries. The knee injury suffered by Patriots tight end Rob Gronkowski on a low hit by Cleveland Browns safety T.J. Ward was cited by several players as an illustration of their concern, including Ward’s teammate Shaun Lauvao.“You saw what happened to Gronkowski,” Lauvao said. “That’s because of a rule change. The way it was before, he would have just got hit in the head. He would have been there for the next play. It’s a Catch-22. I know they’re trying to make it safer, but some rules changes just take away.”NFL senior V.P. of health and safety policy Jeff Miller said that the league took the players’ concerns seriously, but that they wanted to have more hard data before making any decisions about new or different approaches.“When we look at the number of injuries and the types of injuries and the breakdown as to when and where and how those injuries occur, that’s going to inform the decision-making in terms of the health and safety measures that we take,” Miller said. “So if it turns out that the concern that is expressed in your survey is well-founded as we look at the number at the end of the year, then that’s something we’re going to have to address.”It’s a delicate balancing act to pull off in a game that’s going to produce injuries as long as it’s played by excessively large men moving quickly into one another and it figures to be one that the league addresses often in the years to come. read more

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V8 JavaScript Engine releases version 69

first_imgThe newest version of V8 is now out in its beta form and is expected to go fully live with the Chrome 69 Stable in a couple of weeks time. Considering that there is a new branch of V8 created every 6 weeks, this newly released version of the JavaScript Engine has multiple features that will have developers on the lookout for its full and proper release. Here are some of the features that are expected to improve the previously released versions. Embedded built-ins saving memory The newly released version supports a host of built-in functions. Examples are methods on built-in objects such as Array.prototype.sort and RegExp.prototype.exec, but also a wide range of internal functionality. Built–in functions cause a huge overhead as they are complied at build-time. They are then serialized into a snapshot and finally deserialized at runtime to create the initial JS heap state. In the entire process, they consume around 700KB in each Isolate (an Isolate roughly corresponds to a browser tab in Chrome). To combat this issue, V8 V6.4 included lazy deserialization which meant that that each Isolate only paid only for the built-ins that it actually needs (but each Isolate still had its own copy). Embedded built-ins take this feature a notch higher. This is shared by all Isolates, and embedded into the binary itself instead of copied onto the JavaScript heap. This means that built-ins exist in memory only once regardless of how many Isolates are running. This has led to a 9% reduction of the V8 heap size over the top 10k websites on x64. Of these sites, 50% save at least 1.2 MB, 30% save at least 2.1 MB, and 10% save 3.7 MB or more. Improved performance Liftoff, WebAssembly’s new baseline compiler, helps complex websites that use big WebAssmbly modules to start up faster. Depending on the hardware, there is more than 10x increase in the speed of the system when the latest V8 is used. Faster DataView operations DataView methods have been revamped in V6.9. As compared to previous versions, where calling C++ was costly, this new version has reduced the same. Moreover, using DataViews is now proving to be efficient thanks to the inline calls to DataView methods when compiling JavaScript code in TurboFan. This has resulted in better peak performance for hot code. Faster processing of WeakMaps during garbage collection V8 v6.9 also looks at improving WeakMap processing. It reduces Mark-Compact garbage collection pause times thus leading to faster operations. Concurrent and incremental marking can now process WeakMaps. Previously all this work was done in the final atomic pause of Mark-Compact GC. Since moving all of the work outside the pause isn’t suitable, the GC now also does more work in parallel to further reduce pause times. These optimizations essentially halved the average pause time for Mark-Compact GCs in the Web Tooling Benchmark. WeakMap processing uses a fixed-point iteration algorithm. This can degrade to quadratic runtime behavior in certain cases. The new release is now able to switch to another algorithm that is guaranteed to finish in linear time if the GC does not finish within a certain number of iterations. Previously, the GC took a few seconds to finish even with a relatively small heap, while the linear algorithm finishes the processing within a few milliseconds. Head over to the github page for more information on V8. You can also visit the official blog of the V8 javaScript engine for more clarity on the release. Read Next 5 JavaScript machine learning libraries you need to know HTML5 and the rise of modern JavaScript browser APIs [Tutorial] JavaScript async programming using Promises [Tutorial]last_img read more

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How AI is transforming the Smart Cities IoT Tutorial

first_imgAccording to techopedia, a smart city is a city that utilizes information and communication technologies so that it enhances the quality and performance of urban services (such as energy and transportation) so that there’s a reduction in resource consumption, wastage, and overall costs. In this article, we will look at components of a smart city and its AI-powered- IoT use cases, how AI helps with the adaption of IoT in Smart cities, and an example of AI-powered-IoT solution. Deakin and AI Waer list four factors that contribute to the definition of a smart city: Using a wide range of electronic and digital technologies in the city infrastructure Employing Information and Communication Technology (ICT) to transform living and working environment Embedding ICT in government systems Implementing practices and policies that bring people and ICT together to promote innovation and enhance the knowledge that they offer Hence, a smart city would be a city that not only possesses ICT but also employs technology in a way that positively impacts the inhabitants. This article is an excerpt taken from the book ‘Hands-On Artificial Intelligence for IoT’ written by  Amita Kapoor.  The book explores building smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today. Artificial Intelligence (AI), together with IoT, has the potential to address the key challenges posed by excessive urban population; they can help with traffic management, healthcare, energy crisis, and many other issues. IoT data and AI technology can improve the lives of the citizens and businesses that inhabit a smart city.  Let’s see how. Smart city and its AI-powered-IoT use cases A smart city has lots of use cases for AI-powered IoT-enabled technology, from maintaining a healthier environment to enhancing public transport and safety. In the following diagram, you can see some the of use cases for a smart city: Smart city components Let’s have a look at some of the most popular use cases that have already been implemented in smart cities across the world. Smart traffic management AI and IoT can implement smart traffic solutions to ensure that inhabitants of a smart city get from one point to another in the city as safely and efficiently as possible. Los Angeles, one of the most congested cities in the world, has implemented a smart traffic solution to control the flow of traffic. It has installed road-surface sensors and closed-circuit television cameras that send real-time updates about the traffic flow to a central traffic management system. The data feed from the sensors and cameras is analyzed, and it notifies the users of congestion and traffic signal malfunctions. In July 2018, the city further installed Advanced Transportation Controller (ATC) cabinets at each intersection. Enabled with vehicle-to-infrastructure (V2I) communications and 5G connectivity, this allows them to communicate with cars that have the traffic light information feature, such as Audi A4 or Q7. You can learn more about the Los Angeles smart transportation system from their website. The launch of automated vehicles embedded with sensors can provide both the location and speed of the vehicle; they can directly communicate with the smart traffic lights and prevent congestion. Additionally, using historical data, future traffic could be predicted and used to prevent any possible congestion. Smart parking Anyone living in a city must have felt the struggle of finding a parking spot, especially during the holiday time. Smart parking can ease the struggle. With road surface sensors embedded in the ground on parking spots, smart parking solutions can determine whether the parking spots are free or occupied and create a real-time parking map. The city of Adelaide installed a smart parking system in February 2018, they are also launching a mobile app: Park Adelaide, which will provide the user with accurate and real-time parking information. The app can provide users with the ability to locate, pay for, and even extend the parking session remotely. The smart parking system of the city of Adelaide aims to also improve traffic flow, reduce traffic congestion, and decrease carbon emissions. The details of the smart parking system are available in the city of Adelaide website. The San Francisco Municipal Transportation Agency (SAFTA) implemented SFpark a smart parking system. They use wireless sensors to detect real-time parking-space occupancy in metered spaces. Launched in the year 2013, SFpark has reduced weekday greenhouse gas emissions by 25%, the traffic volume has gone down, and drivers’ search time has reduced by 50%. In London, the city of Westminster also established a smart parking system in the year 2014 in association with Machina Research. Earlier, drivers had to wait an average of 12 minutes, resulting in congestion and pollution, but since the installation of the smart parking system, there’s no need to wait; drivers can find an available parking spot using the mobile. These are some of the use-cases mentioned. Other use-cases include smart waste management, smart policing, smart lighting, and smart governance. What can AI do for IoT adaption in smart cities? Building a smart city is not a one-day business, neither is it the work of one person or organization. It requires the collaboration of many strategic partners, leaders, and even citizens. Let’s explore what the AI community can do, what are the areas that provide us with a career or entrepreneurship opportunity. Any IoT platform will necessarily require the following: A network of smart things (sensors, cameras, actuators, and so on) for gathering data Field (cloud) gateways that can gather the data from low power IoT devices, store it, and forward it securely to the cloud Streaming data processor for aggregating numerous data streams and distributing them to a data lake and control applications A data lake for storing all the raw data, even the ones that seem of no value yet A data warehouse that can clean and structure the collected data Tools for analyzing and visualizing the data collected by sensors AI algorithms and techniques for automating city services based on long-term data analysis and finding ways to improve the performance of control applications Control applications for sending commands to the IoT actuators User applications for connecting smart things and citizens Besides this, there will be issues regarding security and privacy, and the service provider will have to ensure that these smart services do not pose any threat to citizens’ wellbeing. The services themselves should be easy to use and employ so that citizens can adopt them. As you can see, this offers a range of job opportunities, specifically for AI engineers. The IoT-generated data needs to be processed, and to benefit from it truly, we will need to go beyond monitoring and basic analysis. The AI tools will be required to identify patterns and hidden correlations in the sensor data. Analysis of historical sensor data using ML/AI tools can help in identifying trends and create predictive models based on them. These models can then be used by control applications that send commands to IoT devices’ actuators. The process of building a smart city will be an iterative process, with more processing and analysis added at each iteration. Let’s now have a look at an example of AI-powered-IoT solution. Detecting crime using San Francisco crime data The San Francisco city also has an open data portal providing data from different departments online. In this section, we take the dataset providing about 12 years (from January 2003 to May 2015) of crime reports from across all of San Francisco’s neighborhoods and train a model to predict the category of crime that occurred. There are 39 discreet crime categories, thus it’s a multi-class classification problem. We will use make use of Apache’s PySpark and use its easy to use text processing features for this dataset. So the first step will be to create a Spark session: The first step is to import the necessary modules and create a Spark session: from pyspark.ml.classification import LogisticRegression as LRfrom pyspark.ml.feature import RegexTokenizer as RTfrom pyspark.ml.feature import StopWordsRemover as SWRfrom pyspark.ml.feature import CountVectorizerfrom pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssemblerfrom pyspark.ml import Pipelinefrom pyspark.sql.functions import colfrom pyspark.sql import SparkSessionspark = SparkSession.builder \.appName(“Crime Category Prediction”) \.config(“spark.executor.memory”, “70g”) \.config(“spark.driver.memory”, “50g”) \.config(“spark.memory.offHeap.enabled”,True) \.config(“spark.memory.offHeap.size”,”16g”) \.getOrCreate() We load the dataset available in a csv file: data = spark.read.format(“csv”). \ options(header=”true”, inferschema=”true”). \ load(“sf_crime_dataset.csv”)data.columns The data contains nine columns: [Dates, Category, Descript, DayOfWeek, PdDistrict, Resolution, Address, X, Y], we will need only Category and Descript fields for training and testing dataset: drop_data = [‘Dates’, ‘DayOfWeek’, ‘PdDistrict’, ‘Resolution’, ‘Address’, ‘X’, ‘Y’]data = data.select([column for column in data.columns if column not in drop_data])data.show(5) Now the dataset we have has textual data, so we will need to perform text processing. The three important text processing steps are: tokenizing the data, remove the stop words and vectorize the words into vectors. We will use RegexTokenizer which will uses regex to tokenize the sentence into a list of words, since punctuation or special characters do not add anything to the meaning, we retain only the words containing alphanumeric content. There are some words like the, which will be very commonly present in the text, but not add any meaning to context. We can remove these words (also called stop words) using the inbuilt StopWordsRemover class. We use standard stop words [“http”,”https”,”amp”,”rt”,”t”,”c”,”the”]. And finally using the CountVectorizer, we convert the words to numeric vector (features). It’s these numeric features that will be used as input to train the model. The output for our data is the Category column, but it’s also textual with 36 distinct categories, and so, we need to convert it to one hot encoded vector; the PySpark’s StringIndexer can be easily used for it. We add all these transformations into our data Pipeline: # regular expression tokenizerre_Tokenizer = RT(inputCol=”Descript”, outputCol=”words”, pattern=”\\W”)# stop wordsstop_words = [“http”,”https”,”amp”,”rt”,”t”,”c”,”the”] stop_words_remover = SWR(inputCol=”words”, outputCol=”filtered”).setStopWords(stop_words)# bag of words countcount_vectors = CountVectorizer(inputCol=”filtered”,outputCol=”features”, vocabSize=10000, minDF=5)#One hot encoding the labellabel_string_Idx = StringIndexer(inputCol = “Category”, outputCol = “label”)# Create the pipelinepipeline = Pipeline(stages=[re_Tokenizer, stop_words_remover,count_vectors, label_string_Idx])# Fit the pipeline to data.pipeline_fit = pipeline.fit(data)dataset = pipeline_fit.transform(data)dataset.show(5) Now, the data is ready, we split it into training and test dataset: # Split the data randomly into training and test data sets.(trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed = 100)print(“Training Dataset Size: ” + str(trainingData.count()))print(“Test Dataset Size: ” + str(testData.count())) Let’s fit a simple logistic regression model for it. On the test dataset, it provides a 97% accuracy. Yahoo!: # Build the modellogistic_regrssor = LR(maxIter=20, regParam=0.3, elasticNetParam=0)# Train model with Training Datamodel = logistic_regrssor.fit(trainingData)# Make predictions on Test Datapredictions = model.transform(testData)# evaluate the model on test data setevaluator = MulticlassClassificationEvaluator(predictionCol=”prediction”)evaluator.evaluate(predictions) AI is changing the way cities operate, deliver, and maintain public amenities, from lighting and transportation to connectivity and health services. However, the adoption can be obstructed by the selection of technology that doesn’t efficiently work together or integrate with other city services. For cities to truly benefit from the potential that smart cities offer, a change in mindset is required. The authorities should plan longer and across multiple departments. The city of Barcelona is a prime example where the implementation of IoT systems created an estimated 47,000 jobs, saved €42.5 million on water, and generated an extra €36.5 million a year through smart parking. We can easily see that cities can benefit tremendously from the technological advances that utilize AI-powered IoT solutions. AI-powered IoT solutions can help connect cities and manage multiple infrastructure, and public services. In this article, we looked at use-cases of smart-cities from smart lighting and road traffic to connected public transport, and waste management. We also learned to use tools that can help categorize the data from the San Francisco crime reports done in a period of 12 years. If you want to explore more topics in the book, be sure to check out the book ‘Hands-On Artificial Intelligence for IoT’. Read Next IBM Watson announces pre-trained AI tools to accelerate IoT operations Implementing cost-effective IoT analytics for predictive maintenance [Tutorial] AI and the Raspberry Pi: Machine Learning and IoT, What’s the Impact?last_img read more

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