Reviewerly/exHarmony huggingface dataset:
Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well.
Noisy label tolerance: A new perspective of Partial Multi-Label Learning

Partial Multi-Label learning (PML) aims to learn from training data where each example is associated with a set of candidate labels, among which only a subset of them is correct. The major challenge of PML lies in that the training procedure is prone to be misled by the label noise. To address this problem, nearly all existing PML methods focus on solely label disambiguation, i.e., dislodging the noisy labels from the candidate label set and then utilizing the remaining credible labels for model induction. However, these remaining “credible” labels may be incorrectly identified, which thereby would have a huge adverse impact on the subsequent model induction. In this paper, in contrary to the above label disambiguation strategy, we propose a simple yet effective Noisy lAbel Tolerated pArtial multi-label Learning (NATAL) method, where the labeling information is considered to be precise while the feature information is assumed to be missing. Using our proposed method, the task of PML can be re-interpreted as a Feature Completion problem, and the desired prediction model can be directly induced from the completed feature together with all candidate labels. Extensive experimental results on various data sets clearly demonstrate the effectiveness of our proposed approach.
Unsupervised Video Summarization With Cycle-Consistent Adversarial LSTM Networks

Video summarization is an important technique to browse, manage and retrieve a large amount of videos efficiently. The main objective of video summarization is to minimize the information loss when selecting a subset of video frames from the original video, hence the summary video can faithfully represent the overall story of the original video. Recently developed unsupervised video summarization approaches are free of requiring tedious annotation on important frames to train a video summarization model and thus are practically attractive. However, their performance is still limited due to the difficulty of minimizing information loss between the summary and original videos. In this paper, we address unsupervised video summarization by developing a novel Cycle-consistent Adversarial LSTM architecture to effectively reduce the information loss in the summary video. The proposed model, named Cycle-SUM, consists of a frame selector and a cycle-consistent learning based evaluator. The selector is a bi-directional LSTM network to capture the long-range relationship between video frames. To overcome the difficulty of specifying a suitable information preserving metric between original video and summary video, the evaluator is introduced to “supervise” selector to improve the video summarization quality. Specifically, the evaluator is composed of two generative adversarial networks (GANs), in which the forward GAN component is learned to reconstruct the original video from summary video, while the backward GAN learns to invert the process. We establish the relation between mutual information maximization and such cycle learning procedure and further introduce cycle-consistent loss to regularize the summarization. Extensive experiments on three video summarization benchmark datasets demonstrate a state-of-the-art performance, and show the superiority of the Cycle-SUM model compared with other unsupervised approaches.
Classification of Patients with Breast Cancer using Neighbourhood Component Analysis and Supervised Machine Learning Techniques

Breast cancer is considered one of the leading causes of death among women. In morocco, the ministry of health reports over 40.000 new cases each year. When lifestyle can be a preventive pattern, early detection remains a factor of a huge impact on the mortality of the diseases. Machine learning (ML) algorithms offer an alternative to breast cancer standard techniques of prediction, or at least can assist radiologists in their reasoning flow and thus saving many females and some males from breast cancer biopsy. The present study represents a benchmarking of different ML models. The research applies and compares four machine learning algorithms (kNN, decision tree, Binary SVM, and Adaboost) to predict whether a patient has a malignant or a benign tumor. The machine learning techniques have been trained then tested on the Breast Cancer Wisconsin dataset. The datasets features are fed into feature selection model with Neighbourhood Components Analysis (NCA) to reduce the number of features and therefore decrease the complexity of the model. The predictive accuracy reached a 99.12% for the kNN model, the best predictive specificity obtained was 9S.S6% for the Binary SVM model and the highest predictive sensitivity obtained was up to one for both kNN and Adaboost models.
Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic

We present a processing pipeline for flow-based traffic classification using a machine learning component leveraging Deep Neural Networks (DNNs). The system is trained to predict likely characteristics of real-world traffic flows from a campus network ahead of time, e.g., a flow's throughput or duration. Training and evaluation of DNN models are continuously performed on a flow data stream collected from a university data center. Instead of the common binary classification into “mice” and “elephant” (throughput) or “short-term” and “long-term” (duration) flows, predicted flow characteristics are quantized into three classes. Various communication contexts (subset of network traffic, e.g., only TCP) and flow feature groups (subset of flow features, e.g., only a flow's 5-tuple), which are supported through an enrichment strategy, are considered and investigated. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. Additionally, we employ an accelerated variant of t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize network traffic data. This enables the understanding of traffic characteristics and relations between communication flows at a glance. Furthermore, possible use-cases and a high-level architecture for flow-based routing scenarios utilizing the developed pipeline are proposed.
Large-Scale High-Utility Sequential Pattern Analytics in Internet of Things

The concepts of sequential pattern mining have become a growing topic in data mining, finding a home most recently in the Internet of Things (IoT) where large volumes of data are presented by the second for analysis and knowledge extraction. One key topic within the realm of sequential pattern mining in high-utility sequential pattern mining (HUSPM), short form for high-utility sequential pattern mining. HUSPM takes into account the fusion of utility and sequence factors to assist in the determination of sequential patterns of high utility from databases and data sources. That being said, almost all current existing literature focus on only using a single machine to increase mining performance. In this work, we present a four-stage MapReduce framework that is solely based on the well-known Spark platform for use in HUSPM. This framework is shown to create a more efficient and faster mining performance for dealing with large data sets. It consists of four phases such as initialization, mining, updating, and generation phases to handle the big data sets based on the MapReduce framework running on the Spark platform. Experiments indicated that the designed model is capable of handling the very big data sets while state-of-the-art algorithms can only achieve good performance in small data sets.
DeepRank: Learning to rank with neural networks for recommendation

Although, widely applied deep learning models show promising performance in recommender systems, little effort has been devoted to exploring ranking learning in recommender systems. It is important to generate a high quality ranking list for recommender systems, whose ultimate goal is to recommend a ranked list of items for users. Also, the latent features learned from Matrix Factorization (MF) based methods do not take into consideration any deep interactions between the latent features; therefore, they are insufficient to capture user–item latent structures. To address these problems, we propose a novel model, DeepRank, which uses neural networks to improve personalized ranking quality for Collaborative Filtering (CF). This is a general architecture that can not only be easily extended to further research and applications, but also be simplified for pair-wise learning to rank. Finally, we perform extensive experiments on three data sets. Results demonstrate that our proposed models significantly outperform the state-of-the-art approaches. Our projects are available at: https://github.com/XiuzeZhou/deeprank.
Effective Contact Tracing for COVID-19 Using Mobile Phones: An Ethical Analysis of the Mandatory Use of the Aarogya Setu Application in India

Abstract Several digital contact tracing smartphone applications have been developed worldwide in the effort to combat COVID-19 that warn users of potential exposure to infectious patients and generate big data that helps in early identification of hotspots, complementing the manual tracing operations. In most democracies, concerns over a breach in data privacy have resulted in severe opposition toward their mandatory adoption. This paper examines India as a noticeable exception, where the compulsory installation of such a government-backed application, the “Aarogya Setu” has been deemed mandatory in certain situations. We argue that the mandatory app requirement constitutes a legitimate public health intervention during a public health emergency.
Generating sequential electronic health records using dual adversarial autoencoder

Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder.We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation.Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients' data.DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.
Artificial Intelligence-Enabled Traffic Monitoring System

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.
Blockchain 2.0: Smart contracts

A smart contract is an agreement among the nodes on the blockchain network. They run on a blockchain and stored it on a public ledger. It is a computer protocol used to digitally facilitate, verify, or enforce the negotiation of a contract. The network transactions are run in a smart contract, which is processed and executed by the blockchain automatically. So, whenever a transaction happens between the nodes, a function is invoked that calls the smart contract, and the processing starts. Hence, the transaction can be submitted to any node on the blockchain, which broadcasts it to the entire network so that all the nodes will see the transaction. With this, we have been discussed the Ethereum platform used for writing smart contracts and introduce the solidity programming language.
Achieving Explainability of Intrusion Detection System by Hybrid Oracle-Explainer Approach

With the progressing development and ubiquitousness of Artificial Intelligence (AI) observed in last decade, the need for creating methods which are explainable and/or interpretable for humans has become a pressing matter. The ability to understand how a system makes a decision is necessary to help develop trust, settle issues of fairness and perform the debugging of a model. Although there are many different techniques allowing to get insights into models' inner workings, they often come with a trade off in the form of decreased accuracy. In the context of cybersecurity, where a single false negative can lead to a breach and compromise of the whole system, such a price is unacceptable. Therefore, there is a need for a solution which allows for the maximum possible model performance, and at the same time delivers human understandable interpretations. The hybrid approaches to Explainable Artificial Intelligence (XAI) have the potential to achieve this goal. In this work, we present the fundamental concepts and a prototype of a system using such an architecture.
Design and Implementation of an Approximate Softmax Layer for Deep Neural Networks

Deep neural networks (DNNs) have been widely used in classification due to their high accuracy. The softmax function is one of the important non-linear functions in DNNs. Therefore, high performance and efficient hardware design are sought. However, the improvement of the softmax function is difficult because the exponent and the division units are complex. In this paper, we propose new approximate hardware architectures for both the exponent and the division units. Compared with the state-of-the-art designs, the proposed approximate softmax design consumes significantly less resources and also achieves high performance while maintaining a very high accuracy.
MommiNet-v2: Mammographic multi-view mass identification networks

Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists’ reading practice. In this paper, we present MommiNet-v2, with improved network architecture and performance. Novel high-resolution network (HRNet)-based architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views for accurate mass detection. A multi-task learning scheme is adopted to incorporate both Breast Imaging-Reporting and Data System (BI-RADS) and biopsy information to train a mass malignancy classification network. Extensive experiments have been conducted on the public DDSM (Digital Database for Screening Mammography) dataset and our in-house dataset, and state-of-the-art results have been achieved in terms of mass detection accuracy. Satisfactory mass malignancy classification result has also been obtained on our in-house dataset.
Semantic Linking Maps for Active Visual Object Search

We aim for mobile robots to function in a variety of common human environments. Such robots need to be able to reason about the locations of previously unseen target objects. Landmark objects can help this reasoning by narrowing down the search space significantly. More specifically, we can exploit background knowledge about common spatial relations between landmark and target objects. For example, seeing a table and knowing that cups can often be found on tables aids the discovery of a cup. Such correlations can be expressed as distributions over possible pairing relationships of objects. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the realworld applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.
Voting-Based Decentralized Consensus Design for Improving the Efficiency and Security of Consortium Blockchain

Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability, and the ability to be tamper proof. These advantages help blockchain become a better option for fields, such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks, such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks, and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this article, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.
Advances in Asynchronous Parallel and Distributed Optimization

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.