Reviewerly/exHarmony huggingface dataset:
A model for distributed in-network and near-edge computing with heterogeneous hardware

Applications that involve analysis of data from distributed networked data sources typically involve computation performed centrally in a datacenter or cloud environment, with some minor pre-processing potentially performed at the data sources. As these applications grow in scale, this centralized approach leads to potentially impractical bandwidth requirements and computational latencies. This has led to interest in edge computing, where processing is moved nearer to the data sources, and recently, in-network computing, where processing is done as data progresses through the network. This paper presents a model for reasoning about distributed computing at the edge and in the network, with support for heterogeneous hardware and alternative software and hardware accelerator implementations. Unlike previous distributed computing models, it considers the cost of computation for compute-intensive applications, supports a variety of hardware platforms, and considers a heterogeneous network. The model is flexible and easily extensible for a range of applications and scales, and considers a variety of metrics. We use the model to explore the key factors that influence where computational capability should be placed and what platforms should be considered for distributed applications.
Single-Stage Joint Face Detection and Alignment

In practice, there are huge demands to localize faces in images and videos under unconstrained pose variation, illumination change, severe occlusion and low resolution, which pose a great challenge to existing face detectors. This challenge report presents a single-stage joint face detection and alignment method. In detail, we employ feature pyramid network, single-stage detection, context modelling, multi-task learning and cascade regression to construct a practical face detector. On the Wider Face Hard validation subset, our single model achieves state-of-the-art performance (92.0% AP) compared with both academic and commercial face detectors for detecting unconstrained faces in cluttered scenes. In the Wider Face AND PERSON CHALLENGE 2019, our ensemble model achieves 56.66% average AP (runner-up) in the face detection track. To facilitate further research on the topic, the training code and models have been provided publicly available.
Inferring Drivers’ Visual Focus Attention Through Head-Mounted Inertial Sensors

Driver distraction is one of the major causes of accidents.Most methods for inferring distracted driving behaviors are vision-based systems that determine the head's orientation.One of the significant challenges of this approach is to develop robust algorithms that detect face and eye features under various lighting conditions.Our approach is based on inferring the vehicle's cabin spot drawing the driver's attention through head-mounted inertial sensors.To achieve this aim, we collected accelerometer, gyroscope, and magnetometer data from ten participants who drove under semi-naturalistic conditions.We generated classifiers by using the Support Vector Machine (SVM linear and RBF), k-nearest neighbor (k-NN), and Random Forest (RF) machine learning techniques.These techniques, except SVM linear, produced an accuracy, precision and recall higher than 96%.Our results demonstrate that raw signals collected from the inertial sensors provide enough information about the head posture associated with the car's cabin spot.
High-order fuzzy clustering algorithm based on multikernel mean shift

This study proposes a method of constructing multikernel space to ensure the integrity of the original data in which the multikernel space aims to reduce the computational complexity of multidimensional data and is suitable for the processing of relational data. The high-dimensional samples of the original space are therefore mapped into a high-dimensional kernel feature space to obtain the inner product. However, when the dimensions of the feature space for multikernel is extremely high or even infinite, the inner product is difficult to calculate directly. To overcome these limitations, this study further proposes a high-order fuzzy clustering (HoFC) algorithm called multikernel mean shift (MKMS-HoFC), which incorporates mean shift based on multikernel space to divide the data and expand the original dimension into multiple new dimensions in the high-dimensional kernel feature space. The MKMS-HoFC initially maps the input points into a high-dimensional feature space of the multikernel and constructs a separating hyper-plane that maximizes the margin among multiple clusters in this space. The multikernel then finds the optimal hyper-plane by HoFC. This method iteratively searches for the densest regions of the sample points in the feature space and improves the clustering performance by using the multidimensional commensurability of HoFC. Real datasets are used to analyze the quality of clustering. Experimental results and comparisons demonstrate the excellent performances of MKMS-HoFC with its effectiveness in practice.
Iris Feature-Based 3-D Gaze Estimation Method Using a One-Camera-One-Light-Source System

Multicamera or multilight-source is generally used to estimate the 3-D coordinates of the cornea center for 3-D gaze estimation in the existing gaze trackers. Although some model-based methods use one-camera systems to estimate 3-D gazes, which include some user-dependent eye parameters preset by fixed values, a one-camera-one-light-source system is still unable to achieve 3-D gaze estimation by considering individual differences. In this article, an iris feature-based method using one camera and one light source for 3-D gaze estimation is proposed. The iris radius and the kappa angle are determined during user calibration. The optical axis is reconstructed by the iris center and its normal vector from only the iris features. Therefore, with the real-time estimation of kappa angle, the 3-D gaze is estimated by an optimization method. Computer simulations and practical experiments have been performed to analyze the feasibility and robustness of the proposed method. This article's innovation lies in achieving 3-D gaze estimation based on only one-camera and one-light source with calibrated eye-specific parameters, which breaks through the limitation of the existing methods using a 3-D cornea center on the system's hardware requirements of 3-D gaze estimation. The simplified hardware system has great application values, especially in the portable mobile devices widely used today. Moreover, a binocular model is used to optimize the results of the point of regard, which improves the accuracy of 3-D gaze estimation effectively.
A Network Framework for Small-Sample Learning

Small-sample learning involves training a neural network on a small-sample data set. An expansion of the training set is a common way to improve the performance of neural networks in small-sample learning tasks. However, improper constraints in expanding training data will reduce the performance of the neural networks. In this article, we present certain conditions for incorporation of additional training data. According to these conditions, we propose a neural network framework for self-training using self-generated data called small-sample learning network (SSLN). The SSLN consists of two parts: the expression learning network and the sample recall generative network, both of which are constructed based on restricted Boltzmann machine (RBM). We show that this SSLN can converge as well as the RBM. Moreover, the experiment results on MNIST Digit, SVHN, CIFAR10, and STL-10 data sets reveal the superiority of the SSLN over other models.
Variations in the Number of Layers and the Number of Neurons in Artificial Neural Networks: Case Study of Pattern Recognition

Abstract This paper presents the analyst the number of layers and the number of neurons in the hidden layer of the Artificial Neural Network. In this study, case studies were taken in the recognition of alphabet patterns and shape patterns. First, the number of layers is varied to get the best number of layers. Furthermore, the number of neurons is varied to get the best number of neurons. The results showed that the best number of layers was 1-5 layers in the hidden layer, with validation values from the recognition system 96-100%. While the best number of neurons is obtained with 19 neurons, with an average accuracy percentage of 81%.
Additional planning with multiple objectives for reinforcement learning

Most control tasks have multiple objectives that need to be achieved simultaneously, while the reward definition is the weighted combination of all objects to determine one optimal policy. This configuration has a limitation in exploration flexibility and presents difficulty in reaching a satisfied terminate condition. Although some multi-objective reinforcement learning (MORL) methods have been presented recently, they concentrate on obtaining a set of compromising options rather than one best-performed strategy. On the other hand, the existing policy-improve methods have rarely emphasized on solving multiple objectives circumstances. Inspired by the enhanced policy search methods, an additional planning technique with multiple objectives for reinforcement learning is proposed in this paper, which is denoted as RLAP-MOP. This method provides opportunities to evaluate parallel requirements at the same time and suggests several optimal feasible actions to improve long-term performance further. Meanwhile, the short-term planning adopted in this paper has advantages in maintaining safe trajectories and building more accurate approximate models, which contributes to accelerating the training program. For comparison, an RLAP with single-objective optimization is also introduced in theoretical and experimental studies. The proposed techniques are investigated on a multi-objective cartpole environment and a soft robotic palpation task. The superiorities in the improved return values and learning stability prove that the multiple objectives based additional planning is a promising assistant to improve reinforcement learning.
A Proposed Mobile Voting Framework Utilizing Blockchain Technology and Multi-Factor Authentication

Abstract Voting is fundamental to any consensus-based society and is one of the most critical functions of democracy. Mobile voting (m-voting) was utilized as a means for voters to easily and conveniently cast their votes using their mobile devices which have been the most adopted means of communication but has a major problem which is safely securing the casted votes and avoiding any form of tampering. In this paper, we propose an m-voting framework that utilizes blockchain technology to securely store the casted votes and multi-factor authentication to authenticate the voters before they cast their votes while also providing an easily accessible, secure and transparent m-voting system.
Sentiment Analysis of Twitter Messages using Word2vec by Weighted Average

Terrorist groups and their supporters use social networks to incite terrorism. They spread their ideas and doctrines by sharing their opinions on these social networks. In this article, we propose a method for detecting radical content in Twitter. For this, we have collected Arabic tweets related to terrorism activities, and classified some of them in two classes of sentiments, inciting terrorism or not inciting. We used Word2vec and Word2vec by weighted average to represent our tweets. In addition, we used two machine learning algorithms, namely SVM and Random Forest to predict sentiments. To validate our methods, we have done some experiments using cross-validation technique. To evaluate our results, we have used three measures. The results revealed that the use of Word2vec by weighted average is slightly better than Word2vec method.
OCR Error Correction for Unconstrained Vietnamese Handwritten Text

Post-processing is an essential step in detecting and correcting errors in OCR-generated texts. In this paper, we present an automatic OCR post-processing model which comprises both error detection and error correction phases for OCR output texts of unconstrained Vietnamese handwriting. We propose a hybrid approach of generating and scoring correction candidates for both non-syllable and real-syllable errors based on the linguistic features as well as the error characteristics of OCR outputs. We evaluate our proposed model on a Vietnamese benchmark database at the line level. The experimental results show that our model achieves 4.17% of character error rate (CER) and 9.82% of word error rate (WER), which helps improve both CER and WER of an attention-based encoder-decoder approach by 0.5% and 3.5% respectively on the VNOnDB-Line dataset of the Vietnamese online handwritten text recognition competition (VOHTR2018). These results outperform those obtained by various recognition systems in the VOHTR2018 competition.
Efficient Approximation of High-Dimensional Functions With Neural Networks

In this article, we develop a framework for showing that neural networks can overcome the curse of dimensionality in different high-dimensional approximation problems. Our approach is based on the notion of a catalog network, which is a generalization of a standard neural network in which the nonlinear activation functions can vary from layer to layer as long as they are chosen from a predefined catalog of functions. As such, catalog networks constitute a rich family of continuous functions. We show that under appropriate conditions on the catalog, catalog networks can efficiently be approximated with rectified linear unit-type networks and provide precise estimates on the number of parameters needed for a given approximation accuracy. As special cases of the general results, we obtain different classes of functions that can be approximated with recitifed linear unit networks without the curse of dimensionality.
Two-stage optimization for machine learning workflow

Abstract Machine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as AutoML . In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.
Multi-Server Private Information Retrieval with Coded Side Information

In this paper, we study the multi-server setting of the Private Information Retrieval with Coded Side Information (PIR-CSI) problem. In this problem, there are K messages replicated across N servers, and there is a user who wishes to download one message from the servers without revealing any information to any server about the identity of the requested message. The user has a side information which is a linear combination of a subset of M messages in the database. The parameter M is known to all servers in advance, whereas the indices and the coefficients of the messages in the user's side information are unknown to any server a priori.We focus on a class of PIR-CSI schemes, referred to as server-symmetric schemes, in which the queries/answers to/from different servers are symmetric in structure. We define the rate of a PIR-CSI scheme as its minimum download rate among all problem instances, and define the server-symmetric capacity of the PIR-CSI problem as the supremum of rates over all server-symmetric PIR-CSI schemes. Our main results are as follows: (i) when the side information is not a function of the user's requested message, the capacity is given by (1 + 1/N + ⋯ + 1/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⌊K/M+1⌋-1</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> for any 1 ≤ M ≤ K- 1; and (ii) when the side information is a function of the user's requested message, the capacity is equal to 1 for M = 2 and M = K, and it is equal to N/(N + 1) for any 3 ≤ M ≤ K- 1. The converse proofs rely on new information-theoretic arguments, and the achievability schemes are inspired by our recently proposed scheme for single-server PIR-CSI as well as the Sun-Jafar scheme for multi-server PIR.
Deep Learning-based Visual Tracking of UAVs using a PTZ Camera System

The visual tracking problem of Unmanned Aerial Vehicles (UAVs) with a Pan-Tilt-Zoom (PTZ) camera system is the subject of this article. Given the background of an image acquired by a PTZ-camera system, a border encompassing a moving object is computed relying on optical flow and the histogram of oriented gradients. Deep Learning (DL) algorithms are trained off-line to decide on the existence of a UAV within this border. Particularly, the ResNet-50 model was trained using a collected data set with more than 50,000 registered positive images. Having identified a UAV, a visual servoing scheme is employed to adjust the PTZ-parameters in order for the border of a detected UAV to span as large as possible the cameras Field of View. The advocated servoing scheme is robust enough against the UAVs rapid maneuvers. Experimental studies are offered to highlight the efficiency of the suggested scheme.
A context‐awareness model for activity recognition in robot‐assisted scenarios

Abstract Context awareness in ambient assisted living programmes for the elderly is a cornerstone in the current scenario of noncustomized service robots distributed around the world. This research proposes a context‐awareness system for a human–robot scene interpretation based on seven primary contexts and the American Occupational Therapy Association. The context‐awareness system defined here proposes an inference mechanism for the activity recognition supported on hierarchical Bayesian networks. However, when the information from sensors increases, the computational cost associated also increases. Thus, an evaluation of different Bayesian network models is necessary for decreasing its impact over the robot performance. Two topological models have been modelled and tested using OpenMarkov application: a two‐level approach of an input–observations layer and the activity recognition layer, and a three‐layer model setting apart a primary contexts layer, the input–observations layer, and the activity recognition layer. The qualitative and quantitative results presented here show better performance in terms of memory and memory in a three‐layer model. Besides, its effect on a hybrid architecture of a robotic platform is presented.
A Multi-Levels Geo-Location based Crawling Method for Social Media Platforms

The large size and the dynamic nature of the Web highlight the need for continuous support and updating of Web based information retrieval systems. Crawlers facilitate the process by following the hyperlinks in Web pages to automatically download a partial snapshot of the Web. While some systems rely on crawlers that exhaustively crawl the Web, others incorporate focus within their crawlers to harvest application or topic specific collections. This project studied web crawling and scraping at many different levels. It will aggregate information from multiple sources into one central location. It Specifics a program for downloading web pages. Given an initial set of seed URLs, it recursively downloads every page that is linked from pages in the set, that have content satisfies specific criterion. Social media, web applications, and mobile applications have been employed together in the proposed system to manage the search in the rapidly growing worldwide web. Applying the proposed system is resulting in a fast and comfortable search engine that fulfill the users requests based on specific geolocations.
Bitcoin Blockchain Compression Algorithm for Blank Node Synchronization

The blockchain provides an immutable, transparent, and decentralized method for data storage. However, as the volume of data gradually increases, the public blockchain system requires significant storage space. Also the synchronization of blank nodes needs much transmission bandwidth. This paper introduces a method to compress the size of transactions in the Bitcoin blockchain by replacing hash pointers with index pointers. Simulations show that the proposed method can reduce the storage space of the Bitcoin blockchain by up to 12.71%.
Disguised Faces in the Wild 2019

Disguised face recognition has wide-spread applicability in scenarios such as law enforcement, surveillance, and access control. Disguise accessories such as sunglasses, masks, scarves, or make-up modify or occlude different facial regions which makes face recognition a challenging task. In order to understand and benchmark the state-of-the-art on face recognition in the presence of disguise variations, the Disguised Faces in the Wild 2019 (DFW2019) competition has been organized. This paper summarizes the outcome of the competition in terms of the dataset used for evaluation, a brief review of the algorithms employed by the participants for this task, and the results obtained. The DFW2019 dataset has been released with four evaluation protocols and baseline results obtained from two deep learning-based state-of-the-art face recognition models. The DFW2019 dataset has also been analyzed with respect to degrees of difficulty: (i) easy, (ii) medium, and (iii) hard. The dataset has been released as part of the International Workshop on Disguised Faces in the Wild at International Conference on Computer Vision (ICCV), 2019.
Internet of Things Platform for Transparency and Traceability of Food Supply Chain

The Internet of things (IoT) platform can provide product provenance (information that traces a product to its origin) in the food supply chain (FSC). This empowers consumers with the information and opportunity to make the most informed decision. Integrating IoT technology with blockchain can make FSC transparent, making it more productive by providing trustworthy and secure information to both consumers and relevant parties. This paper proposes an architecture of a blockchain- enabled IoT platform to replace the third party present in the FSC, which would authorize the product data. This platform will ensure that the product data collected through sensors from each stage of the supply chain is legitimate and adheres to terms agreed by all parties involved in the system. We present a case study to support the idea of accessible IoT technology being used to create a data network. It details which different technologies that can be used in IoT platform and how it supports the proposed architecture. We show how blockchain can be used in tangent with IoT technologies in order to modernize and optimize the standard FSC.