The era of information explosion had prompted the accumulation of a tremendous amount of time-series data, including stationary and non-stationary time-series data. State-of-the-art algorithms have achieved a decent performance in dealing with stationary temporal data. However, traditional algorithms that tackle stationary time-series do not apply to non-stationary series like Forex trading. This paper investigates applicable models that can improve the accuracy of forecasting future trends of non-stationary time-series sequences. In particular, we focus on identifying potential models and investigate the effects of recognizing patterns from historical data. We propose a combination of \rebuttal{the} seq2seq model based on RNN, along with an attention mechanism and an enriched set features extracted via dynamic time warping and zigzag peak valley indicators. Customized loss functions and evaluating metrics have been designed to focus more on the predicting sequence's peaks and valley points. Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making. We further provide evaluations of the effects of various loss functions, evaluation metrics, model variants, and components on model performance.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary area with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the area. However, due to a surge of new researchers joining the area in recent years, the necessity for a comprehensive survey of the area has become extremely important. Therefore, amongst other aspects of the area, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners.

While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.

Ask Pixel 6 owners about their top gripe and they'll likely point to the slow, finicky fingerprint sensor. There may be an explanation for that momentary anguish, though. Google is telling users that the Pixel 6's fingerprint reader is using "enhanced security algorithms" that may either take longer to check your digits or require better sensor contact. We've asked Google for comment. Some users have suggested the sluggish performance might be due to Google's use of an optical under-display fingerprint reader instead of the ultrasonic sensor found in phones like the Galaxy S21. However, Reddit users noted there are phones with optical sensors that perform faster, such as the OnePlus 9. There's a real chance software may play a role in the Pixel 6's quirks.

This article is the final article in the 3-part series for Image Recognition in WhatsApp Chatbot. The first article, LUIS – Create a Conversation App discussed more on creating a service in Azure for LUIS. The second article Image Recognition in WhatsApp Chatbot - Using Azure AI continued on to create models and Image Recognition service on Visual Studio. This last article focuses on using Twilio and Azure function app to develop the WhatsApp Chatbot. Twilio offers the service of cloud communication platform (CPaaS) to enable developers to make and receive phone calls programmatically, send and receive messages in text format as well as perform numerous other communication functionalities through web service APIs.

This post is based on Simon Mo's "Patterns of Machine Learning in Production" talk from Ray Summit 2021. Over the past couple years, we've listened to ML practitioners across many different industries to learn and improve the tooling around ML production use cases. Through this, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready. It is a scalable and programmable serving framework built on top of Ray to help you scale your microservices and ML models in production.

In the last article, we learnt about LUIS - Language Understanding Intelligent Service provided by Azure and then learnt to create a conversation app. This was fundamental to create a cognitive service in Azure such that we can obtain a subscription key and endpoint to use in our application. This article focuses on following up on the app created in Azure to make a full-fledged AI Chatbot. We can learn about all these services provided in Azure for Machine Learning through the article, Azure Cognitive Services. Also read the last article, Luis – Create a conversation app this follows up on.

Prediction and description are not equivalently important for every data mining application. In the context of Knowledge Discovery in Databases, description tends to be more important than prediction. Prediction and description are achieved by using various data mining tasks. Depending on the nature of the data and the desired knowledge there is a large variety of algorithms for each task.

Google says minors and their families can ask for an image to be removed from its search results, in a new policy unveiled Wednesday. Google says minors and their families can ask for an image to be removed from its search results, in a new policy unveiled Wednesday. Google installed a new policy Wednesday that will allow minors or their caregivers to request their images be removed from the company's search results, saying that "kids and teens have to navigate some unique challenges online, especially when a picture of them is unexpectedly available on the internet." The policy follows up on Google's announcement in August that it would take a number of steps aiming to protect minors' privacy and their mental well-being, giving them more control over how they appear online. Google says the process for taking a minor's image out of its search results starts with filling out a form that asks for the URL of the target image.