Now we want to use this model for trading under a commercial trading platform and see if it is going to generate a profit. The techniques used in this story are focusing on the model in my previous story, but they can be tweaked to fit another model. The intention here is to make the model usable by other systems, e.g. a trading platform. You can find the source code for this story in directories "LSTM-FX-CTrader-Client" and "LSTM-FX-Prediction-Server" here: In the previous story, we have trained and tested a model and saved the resulting model as a directory and the scaler used for the data as a file. The model and the scaler are the only items that we need, in addition to understanding the input and the output parameters.

Deep-AI Technologies is emerging from stealth and launching the industry's first integrated training and inference solution for deep learning at the edge. With Deep-AI, every inference node at the edge also becomes a training node, enabling faster, cheaper, scalable, and more secure AI versus today's cloud-centric AI approach. Deep-AI's solution runs on off-the-shelf FPGA cards, eliminating the need for GPUs, and provides a 10X gain in performance/power or performance/cost versus a GPU. The FPGA hardware is completely under-the-hood and transparent to the data scientists and the developers designing their AI applications. Standard deep learning frameworks are supported including Tensorflow, PyTorch and Keras.

Be it healthcare, travel, fitness, finance, or any other industry, the advent of artificial intelligence has revolutionized almost all of them by enabling machines to act and behave like human beings. When this technology is modernizing every sector, how can the content industry be left behind? Artificial intelligence has also created a major disruption with content generator tools. Content is the king; every website, blog, or mobile application needs content to connect with their targeted audience. Content is available everywhere on the web. Now, writing this content requires a professional with writing skills and a sound knowledge of the guidelines mentioned by search engines.

The goal of the project is to read the exact consumption of water in cubic meters. As shown on the picture below, the cubic meters are written in white on a black background. As YOLO is one of the best Convolutional Neural Network (CNN) algorithms for object detection, we decided to implement our model with a Pytorch version of YOLOv4. We defined 12 classes of objects that we want to detect on the pictures: the 10 digits from 0 to 9, the part of the meter corresponding to the liters, and the whole counter. We labelled each picture by drawing the bounding boxes of each object found.

We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions of each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS, i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to not only learn to generate captions but also predict the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. We additionally extend analysis to an ablation study, applications on holistic image captioning, scene graph generation, and retrieval tasks.

Earlier Eye-tracking was mainly used in studies of psychophysics or cognitive development, in the last decade, the reduction in the cost of technological equipment and advances in machine vision has allowed this technology to go far beyond the scope of disease diagnosis. Today Eye-tracking is used to support multimedia learning, help in browsing the web, and is widely used in real-time graphics systems, which is especially popular for video games. The main problem of modern Eye-tracking systems is their high price. Equipment with viewing angle accuracy of less than 0.5 has prices from several thousand dollars. In recent years, more than 800 peer-reviewed manuscripts published on the topic of eye-tracking in popular libraries. In the last 5 years, thanks to the development of deep learning in the field of machine vision, happened a jump, which allowed for the creation of eye-tracking devices in the low-cost range.

It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform designed to improve learning outcomes (LOs) must provide actionable inputs to the learner in these dimensions. However, operationalizing such inputs in a production setting that is scalable is not trivial. We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student. ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges, both critical to improving LOs. Multiple machine learning models are evaluated for the prediction task. In order to provide meaningful feedback to the learner, individualized Shapley feature attributions for each feature are computed. Prediction intervals are obtained by applying non-parametric quantile regression, in an attempt to quantify the uncertainty in the predictions. We apply the above modelling strategy on a dataset consisting of more than a hundred million learner interactions on the Embibe learning platform. We observe that the Median Absolute Error between the observed and predicted scores is 4.58% across several user segments, and the correlation between predicted and observed responses is 0.93. Game-like what-if scenarios are played out to see the changes in LOs, on counterfactual examples. We briefly discuss how a rational agent can then apply an optimal policy to affect the learning outcomes by treating the above model like an Oracle.

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.

An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language understanding tasks. It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data. Such an encryption step is efficient and only affects the task performance slightly. In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task. We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only $1.9\%$. We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem. Our code is available at https://github.com/Hazelsuko07/TextHide

Cell complexes are topological spaces constructed from simple blocks called cells. They generalize graphs, simplicial complexes, and polyhedral complexes that form important domains for practical applications. We propose a general, combinatorial, and unifying construction for performing neural network-type computations on cell complexes. Furthermore, we introduce inter-cellular message passing schemes, message passing schemes on cell complexes that take the topology of the underlying space into account. In particular, our method generalizes many of the most popular types of graph neural networks.