Reviewerly/exHarmony huggingface dataset:
Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in about fifteen articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.
Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning

This research focuses on evaluating whether a website is legitimate or phishing.Our research contributes to improving the accuracy of phishing website detection.Hence, a feature selection algorithm is employed and integrated with an ensemble learning methodology, which is based on majority voting, and compared with different classification models including Random forest, Logistic Regression, Prediction model etc.Our research demonstrates that current phishing detection technologies have an accuracy rate between 70% and 92.52%.The experimental results prove that the accuracy rate of our proposed model can yield up to 95%, which is higher than the current technologies for phishing website detection.Moreover, the learning models used during the experiment indicate that our proposed model has a promising accuracy rate.
Generation and Processing of Complex Photon States With Quantum Frequency Combs

The development of technologies for quantum information (QI) science demands the realization. and precise control of complex (multipartite and high dimensional) entangled systems on practical and scalable platforms. Quantum frequency combs (QFCs) represent a powerful tool towards this goal. They enable the generation of complex photon states within a single spatial mode as well as their manipulation using standard fiber-based telecommunication components. Here, we review recent progress in the development of QFCs, with a focus on results that highlight their importance for the realization of complex quantum states. In particular, we outline recent work on the use of integrated QFCs for the generation of high-dimensional multipartite optical cluster states – lying at the basis of measurement-based quantum computation. These results confirm that the QFC approach can provide a stable, practical, low-cost, and established platform for the development of quantum technologies, paving the way towards the advancement of QI science for out-of-the-lab applications, ranging from practical quantum computing to more secure communications.
Factual Error Correction for Abstractive Summarization Models

Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods.However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge.We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries.The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries.These transformations are inspired by an error analysis of state-of-the-art summarization model outputs.Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.We also find that transferring from artificial error correction to downstream settings is still very challenging 1 .
Deep Federated Learning Enhanced Secure POI Microservices for Cyber-Physical Systems

An essential consideration in cyber-physical systems (CPS) is the ability to support secure communication services, such as points of interest (POI) microservices. Existing approaches to support secure POI microservices generally rely on anonymity and/or differential privacy technologies. There are, however, a number of known limitations with such approaches. Hence, this work presents a deep-federated-learning-based framework for securing POI microservices in CPS. In order to enhance data security, the system architecture is designed to isolate the cloud center from accessing user data on edge nodes, and an interactive training mechanism is introduced between the cloud center and edge nodes. Specifically, edge nodes pre-train reliable deep-learning-based models for users, and the cloud server coordinates parameter updating via federated learning. The connected and isolated structure between cloud center and edges facilitates deep federated learning. Finally, we implement and evaluate the performance of our proposed approach using two real-world POI-related datasets. The results show that our proposed approach achieves optimal scheduling performance and demonstrates its practical utility.
Analysis of the AutoML Challenge Series 2015–2018

The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/ .
Infinite Mixture Prototypes for Few-Shot Learning

We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster. By inferring the number of clusters, infinite mixture prototypes interpolate between nearest neighbor and prototypical representations, which improves accuracy and robustness in the few-shot regime. We show the importance of adaptive capacity for capturing complex data distributions such as alphabets, with 25% absolute accuracy improvements over prototypical networks, while still maintaining or improving accuracy on the standard Omniglot and mini-ImageNet benchmarks. In clustering labeled and unlabeled data by the same clustering rule, infinite mixture prototypes achieves state-of-the-art semi-supervised accuracy. As a further capability, we show that infinite mixture prototypes can perform purely unsupervised clustering, unlike existing prototypical methods.
Cycle-consistent Conditional Adversarial Transfer Networks

Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions. Recently, adversarial training have been successfully applied to domain adaptation and achieved state-of-the-art performance. However, there is still a fatal weakness existing in current adversarial models which is raised from the equilibrium challenge of adversarial training. Specifically, although most of existing methods are able to confuse the domain discriminator, they cannot guarantee that the source domain and target domain are sufficiently similar. In this paper, we propose a novel approach named cycle-consistent conditional adversarial transfer networks (3CATN) to handle this issue. Our approach takes care of the domain alignment by leveraging adversarial training. Specifically, we condition the adversarial networks with the cross-covariance of learned features and classifier predictions to capture the multimodal structures of data distributions. However, since the classifier predictions are not certainty information, a strong condition with the predictions is risky when the predictions are not accurate. We, therefore, further propose that the truly domain-invariant features should be able to be translated from one domain to the other. To this end, we introduce two feature translation losses and one cycle-consistent loss into the conditional adversarial domain adaptation networks. Extensive experiments on both classical and large-scale datasets verify that our model is able to outperform previous state-of-the-arts with significant improvements.
On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward

The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand. The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.
Joint Entity Linking with Deep Reinforcement Learning

Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the final result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions. We conduct experiments on different types of datasets, the results show that our model outperforms state-of-the-art systems and has better generalization performance.
Min-Entropy Latent Model for Weakly Supervised Object Detection

Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak supervision and learning objectives introduces significant randomness to object locations and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is proposed for weakly supervised object detection. Min-entropy serves as a model to learn object locations and a metric to measure the randomness of object localization during learning. It aims to principally reduce the variance of learned instances and alleviate the ambiguity of detectors. MELM is decomposed into three components including proposal clique partition, object clique discovery, and object localization. MELM is optimized with a recurrent learning algorithm, which leverages continuation optimization to solve the challenging non-convexity problem. Experiments demonstrate that MELM significantly improves the performance of weakly supervised object detection, weakly supervised object localization, and image classification, against the state-of-the-art approaches.
Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept and 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed, which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets, and the results show that our method significantly outperforms the state of the art.
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network

Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks. This prevents such policies from being applied to more complex multi-agent tasks. To resolve these limitations, we propose a model that conducts both representation learning for multiple agents using hierarchical graph attention network and policy learning using multi-agent actor-critic. The hierarchical graph attention network is specially designed to model the hierarchical relationships among multiple agents that either cooperate or compete with each other to derive more advanced strategic policies. Two attention networks, the inter-agent and inter-group attention layers, are used to effectively model individual and group level interactions, respectively. The two attention networks have been proven to facilitate the transfer of learned policies to new tasks with different agent compositions and allow one to interpret the learned strategies. Empirically, we demonstrate that the proposed model outperforms existing methods in several mixed cooperative and competitive tasks.
TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text.However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.
Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition

CNNs have rapidly become state-of-the-art frameworks for various applications used in image classification. We typically need big, ground-based training data set so as to use revolutionary techniques in making a model more complex without affecting its accuracy and precision. In this case study, we shall discuss the different approaches and measures that are used in pattern recognition using CNN. We shall also discuss some of the major applications used nowadays in pattern recognition as well as their implementation (outputs only). We shall identify the key features used in building that application as well as its roles in making the application fit from any obstacles such as over-fitting, etc.
Few-NERD: A Few-shot Named Entity Recognition Dataset

Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task.Current approaches collect existing supervised NER datasets and reorganize them into the few-shot setting for empirical study.These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained.In this paper, we present FEW-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 finegrained entity types.FEW-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type.To the best of our knowledge, this is the first few-shot NER dataset and the largest humancrafted NER dataset.We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models.Extensive empirical results and analysis show that FEW-NERD is challenging and the problem requires further research.We make FEW-NERD public at https:// ningding97.github.io/fewnerd/.
A Novel and Efficient Method to Detect the Face Coverings to Ensurethe Safety using Comparison Analysis

A face mask worn by a single person or a group of individuals can be identified using face mask detection, an AI-based system that examines a video stream. A confidence value is produced for each detection by our DeepSight programme. Either a person is labeled as "wearing a mask" or "not wearing a mask" depends on their classification. The World Health Organization (WHO) recommends that a face mask completely cover the face, including the chin and nose. Face Mask Detection System may be used with both CCTV cameras and already-installed USB or IP cameras. To distinguish between persons wearing masks and those who aren’t, it employs the most recent computer vision techniques. Compact model’s accuracy is really great, and it can tell if you’re wearing a mask properly or not (i.e.; mask covering the nose). Face mask detection data may be used to a wide range of settings and sectors, including corporate offices, shops, public transit, airports, and taxis. The main motive of the paper by the author is represented as solving the problem using cnn and transfer learning and also a comparison between both the approaches.
BlockEdge: Blockchain-Edge Framework for Industrial IoT Networks

Industry 4.0 encompasses a promise of a new industrial revolution in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Key industrial applications and systems will be significantly more complex due to the involvement of the vast number of different devices and diverse nature of various stakeholders and service providers. These complex industrial processes, services and applications also have strict requirements in terms of performance - latency in particular - and resource-efficiency, together with high standards for security and trust. In this context, Blockchain and Edge Computing emerge as prominent technologies to address the mentioned essential requirements and to further strengthen the rise of the new era of digitization. The Edge computing paradigm ensures low latency services for IIoT applications while optimizing the network usage, whereas Blockchain provides a decentralized way for ensuring data integrity, trust and security. In this paper, we propose a `BlockEdge' framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We verify the feasibility of our approach by evaluating the performance and resource-efficiency of BlockEdge in terms of latency, power consumption and network usage, through simulations against non-Blockchain solution.
Serverless Applications: Why, When, and How?

Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as 'Why do so many companies adopt serverless?', 'When are serverless applications well suited?', and 'How are serverless applications currently implemented?' To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing - the most extensive study to date.
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.