Reviewerly/exHarmony huggingface dataset:
E-Learning Recommender System for Learners: A Machine Learning based Approach

Web mining procedure helps the surfers to get the required information but finding the exact information is as good as finding a needle in a haystack. In this work, an intelligent prediction model using Tensor Flow environment for Graphics Processing Unit (GPU) devices has been designed to meet the challenges of speed and accuracy. The proposed approach is isolated into two stages: pre-processing and prediction. In the first phase, the procedure starts via looking through the URLs of various e-learning sites particular to computer science subjects. At that point, the content of looked through URLs are perused and after that from their keywords are produced identified with a particular subject in the wake of playing out the pre-processing of the content. Second phase is prediction that predicts query specific links of e-learning website. The proposed Intelligent E-learning through Web (IEW) has content mining, lexical analysis, classification and machine learning based prediction as its key features. Algorithms like SVM, Naïve Bayes, K-Nearest Neighbor, and Random Forest were tested and it was found that Random Forest gave an accuracy of 98.98%, SVM 42%, KNN 63% and Naïve Bayes 66%. Based on the results IEW uses Random forest for prediction.
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps

Recent works have shown Generative Adversarial Networks (GANs) to be particularly effective in image-to-image translations. However, in tasks such as body pose and hand gesture translation, existing methods usually require precise annotations, e.g. key-points or skeletons, which are time-consuming to draw. In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture. Our architecture synthesizes the target gesture while preserving the background context, thus effectively dealing with gesture translation in the wild. To this aim, we use an attention module and a rolling guidance approach, which loops the generated images back into the network and produces higher quality images compared to competing works. Thus, our GAN learns to generate new images from simple annotations without requiring key-points or skeleton labels. Results on two public datasets show that our method outperforms state of the art approaches both quantitatively and qualitatively. To the best of our knowledge, no work so far has addressed the gesture-to-gesture translation in the wild by requiring user-friendly annotations.
IoT Sensing Networks for Gait Velocity Measurement

Abstract Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5◦.
Classification study for the imbalanced data based on Biased-SVM and the modified over-sampling algorithm

By combining the modified Random-SMOTE oversampling algorithm with the Biased-SVM classification method, this paper has proposed an improved classification approach for the imbalanced data sets. This algorithm is able to cluster the minority samples, and ensures the support vectors as the parent samples according to the distances between the cluster centers in the minority class and the majority class center. It then could generate the new samples for the minority class. The experiment is conducted upon the five imbalanced data sets from UCI data set and the proposed algorithm is compared with other algorithms. The experimental results show that the improved algorithm has significant classification effect for the imbalanced data sets.
Acceleration of Deep Recurrent Neural Networks with an FPGA cluster

In this paper, we propose an acceleration methodology for deep recurrent neural networks (RNNs) implemented on a multi-FPGA platform called Flow-in-Cloud (FiC). RNNs have been proven effective for modeling temporal sequences, such as human speech and written text. However, the implementation of RNNs on traditional hardware is inefficient due to their long-range dependence and irregular computation patterns. This inefficiency manifests itself in the proportional increase of run time with respect to the number of layers of deep RNNs when running on traditional hardware platforms such as a CPUs. Previous works have mostly focused on the optimization of a single RNN cell. In this work, we take advantage of the multi-FPGA system to demonstrate that we can reduce the run time of deep RNNs from O(k) to O(1).
Modified dragonfly algorithm based multilevel thresholding method for color images segmentation

Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.
A Survey on Digital Image Steganography

Internet brings us not only the convenience of communication but also some security risks, such as intercepting information and stealing information. Therefore, some important information needs to be hidden during communication. Steganography is the most common information hiding technology. This paper provides a literature review on digital image steganography. The existing steganography algorithms are classified into traditional algorithms and deep learning-based algorithms. Moreover, their advantages and weaknesses are pointed out. Finally, further research directions are discussed.
On PAC-Bayesian bounds for random forests

Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying. We discuss and evaluate various PAC-Bayesian approaches to derive such bounds. The bounds do not require additional hold-out data, because the out-of-bag samples from the bagging in the training process can be exploited. A random forest predicts by taking a majority vote of an ensemble of decision trees. The first approach is to bound the error of the vote by twice the error of the corresponding Gibbs classifier (classifying with a single member of the ensemble selected at random). However, this approach does not take into account the effect of averaging out of errors of individual classifiers when taking the majority vote. This effect provides a significant boost in performance when the errors are independent or negatively correlated, but when the correlations are strong the advantage from taking the majority vote is small. The second approach based on PAC-Bayesian $$C$$ -bounds takes dependencies between ensemble members into account, but it requires estimating correlations between the errors of the individual classifiers. When the correlations are high or the estimation is poor, the bounds degrade. In our experiments, we compute generalization bounds for random forests on various benchmark data sets. Because the individual decision trees already perform well, their predictions are highly correlated and the $$C$$ -bounds do not lead to satisfactory results. For the same reason, the bounds based on the analysis of Gibbs classifiers are typically superior and often reasonably tight. Bounds based on a validation set coming at the cost of a smaller training set gave better performance guarantees, but worse performance in most experiments.
Make Skeleton-based Action Recognition Model Smaller, Faster and Better

Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition. By using a lightweight network structure (i.e., 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500 FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net achieves the state-of-the-art performance on our experimental datasets: SHREC (i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released with this paper later.
The Influence of Down-Sampling Strategies on

The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding
Data modeling for quality prediction using improved orthogonal incremental random vector functional-link networks

Innumerable complex industrial processes, such as blast furnace (BF) ironmaking process, greatly rely on some well-performing quality models to realize prediction and control of product quality. This paper proposes an improved orthogonal incremental random vector functional-link networks (I-OI-RVFLNs) algorithm with compact structure and high computational efficiency and applies it to quality modeling of BF ironmaking process. First, in order to improve the convergence speed of the basic I-RVFLNs, Schmidt orthogonalization method is introduced to orthogonalize the output vectors of the hidden layer nodes obtaining the least squares solution. Then, the proposed I-OI-RVFLNs algorithm further improves the network construction mode of the existing I-RVFLNs simplifying the network structure. To cut out the hidden nodes with smaller output weights, the number of hidden nodes of the proposed method is first fixed in advance and then the output weights of each hidden node are iteratively updated to approximate the network output. In this way, the problem of low computational efficiency caused by too many hidden nodes in basic I-RVFLNs can be avoided in the proposed algorithm. Lastly, data experiments using actual industrial data show that the proposed algorithm has much better performance in terms of convergence speed and computational efficiency than other compared algorithms.
Efficient Identification of Approximate Best Configuration of Training in Large Datasets

A configuration of training refers to the combinations of feature engineering, learner, and its associated hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently identify the best configuration with approximately the highest testing accuracy when trained from the training set. To guarantee small accuracy loss, we develop a solution using confidence interval (CI)-based progressive sampling and pruning strategy. Compared to using full data to find the exact best configuration, our solution achieves more than two orders of magnitude speedup, while the returned top configuration has identical or close test accuracy.
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally intensive, is serial in nature and mixes poorly between posterior modes. Further, all models are misspecified, which brings into question the validity of the conventional Bayesian update. We present a scalable Bayesian nonparametric learning routine that enables posterior sampling through the optimization of suitably randomized objective functions. A Dirichlet process prior on the unknown data distribution accounts for model misspecification, and admits an embarrassingly parallel posterior bootstrap algorithm that generates independent and exact samples from the nonparametric posterior distribution. Our method is particularly adept at sampling from multimodal posterior distributions via a random restart mechanism. We demonstrate our method on Gaussian mixture model and sparse logistic regression examples.
Research review on augmented reality as an educational resource for people with intellectual disabilities

The present study reviewed nine publications related to the application of augmented reality (AR)-based interventions to people with intellectual disabilities (ID).The lines of research developed focus on the areas of education, personal autonomy and health promotion.The studies are theoretical frameworks, design of AR-based applications, or quasi-experimental study designs without a control group.The samples, all of less than 15 subjects, have been chosen under intentional criteria.The devices that seem to prevail as preferred for its application in this group are the portable ones due to the wide range of intervention possibilities that they offer as well as the development potential of the personal autonomy that they allow.Results offer a positive view of the application of this technology to people with ID, although the characteristics of the studies do not allow yet its generalization.Among the reported benefits are: enhancing learning achievement, motivation and enjoyment of tasks, helping to understand information, enhancing orientation, raising the level of engagement.
OrpailleCC: a Library for Data Stream Analysis on Embedded Systems

The Internet of Things could benefit in several ways from mining data streams on connected objects rather than in the cloud.In particular, limiting network communication with cloud services would improve user privacy and reduce energy consumption in connected devices.Besides, applications could leverage the computing power of connected objects for improved scalability.
Hardware, apps, and surveys at scale

The vision of sensor systems that collect critical and previously ungathered information about the world is often only realized when sensors, students, and subjects move outside the academic laboratory. However, deployments at even the smallest scales introduce complexities and risks that can be difficult for a research team to anticipate. Over the past year, our interdisciplinary team of engineers and economists has been designing, deploying, and operating a large sensor network in Accra, Ghana that measures power outages and quality at households and firms. This network consists of 457 custom sensors, over 3,000 mobile app instances, thousands of participant surveys, and custom user incentive and deployment management systems. In part, this deployment supports an evaluation of the impacts of investments in the grid on reliability and the subsequent effects of improvements in reliability on socioeconomic well-being. We report our experiences as we move from performing small pilot deployments to our current scale, attempting to identify the pain points at each stage of the deployment. Finally, we extract high-level observations and lessons learned from our deployment activities, which we wish we had originally known when forecasting budgets, human resources, and project timelines. These insights will be critical as we look toward scaling our deployment to the entire city of Accra and beyond, and we hope that they will encourage and support other researchers looking to measure highly granular information about our world's critical systems.
Middleware to Operate Smart Photovoltaic Irrigation Systems in Real Time

Climate change, water scarcity and higher energy requirements and electric tariff compromises the continuity of the irrigated agriculture. Precision agriculture (PA) or renewable energy sources which are based on communication and information technologies and a large amount of data are key to ensuring this economic activity and guaranteeing food security at the global level. Several works which are based on the use of PA and renewable energy sources have been developed in order to optimize different variables of irrigated agriculture such as irrigation scheduling. However, the large amount of technologies and sensors that these models need to be implemented are still far from being easily accessible and usable by farmers. In this way, a middleware called Real time Smart Solar Irrigation Manager (RESSIM) has been developed in this work and implemented in MATLABTM with the aim to provide to farmers a user-friendly tool for the daily making decision process of irrigation scheduling using a smart photovoltaic irrigation management module. RESSIM middleware was successfully tested in a real field during a full irrigation season of olive trees using a real smart photovoltaic irrigation system.
Mindful Active Learning

We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes physical and cognitive limitations of the oracle into account when selecting sensor data to be annotated by the oracle. Our approach is inspired by human-beings' limited capacity to respond to external stimulus such as responding to a prompt on their mobile devices. This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the lag between the time that a query is made and when it is responded to. We introduce the notion of mindful active learning and propose a computational framework, called EMMA, to maximize the active learning performance taking informativeness of sensor data, query budget, and human memory into account. We formulate this optimization problem, propose an approach to model memory retention, discuss complexity of the problem, and propose a greedy heuristic to solve the problem. We demonstrate the effectiveness of our approach on three publicly available datasets and by simulating oracles with various memory strengths. We show that the activity recognition accuracy ranges from 21% to 97% depending on memory strength, query budget, and difficulty of the machine learning task. Our results also indicate that EMMA achieves an accuracy level that is, on average, 13.5% higher than the case when only informativeness of the sensor data is considered for active learning. Additionally, we show that the performance of our approach is at most 20% less than experimental upper-bound and up to 80% higher than experimental lower-bound. We observe that mindful active learning is most beneficial when query budget is small and/or oracle's memory is weak, thus emphasizing contributions of our work in human-centered mobile health settings and for elderly with cognitive impairments.
Intelligent Robot Arm: Vision-Based Dynamic Measurement System for Industrial Applications

Current industrial robot arms are not satisfied in flexibility and intelligence due to the lack of visual perception. The production efficiency also runs into a bottleneck because most parts must be completely fixed when assembling and welding. We propose a vision-based intelligent robot arm, which can dynamically sense the environment and perform appropriate operations. The measurement system consists of operator face authentication, gesture remote control, abnormal entry detection and moving target tracking. The capabilities of human-machine interaction and dynamic measurement meet the needs of high-performance robot arm in intelligent manufacturing, with the characteristics of intelligence, safety, efficiency and flexibility. Various functions of the intelligent robot arm are verified through a large number of experiments in laboratory.
An ADMM-based scheme for distance function approximation

Abstract A novel variational problem for approximating the distance function (to a domain boundary) is proposed. It is shown that this problem can be efficiently solved by ADMM. A review of several other variational and PDE-based methods for distance function estimation is presented. Advantages of the proposed distance function estimation method are demonstrated by numerical experiments. Applications of the method to the problems of surface curvature estimation and computing the skeleton of a binary image are shown.
Efficient Direct Measurement of Arbitrary Quantum Systems via Weak Measurement

Efficient, reliable determination of a system's state is at the heart of quantum science. Directly measuring any desired density-matrix elements is a unique challenge for both theorists and experimentalists, as standard methods have always suffered from the rapid increase in complexity of measurements and reconstruction algorithms. This study presents an efficient scheme for directly measuring arbitrary density matrices with only one strong measurement, or a weak measurement, of each qudit. This method is important for characterizing large-scale quantum systems, and the technology that will be derived from it will be easy to expand and integrate, for example, on a quantum chip.