Reviewerly/exHarmony huggingface dataset:
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
A K-Anonymity Based Schema for Location Privacy Preservation

In recent years, with the development of mobile devices, the location based services (LBSs) have become more and more prevailing and most applications installed on these devices call for location information. Yet, the untrusted LBS provider can collect this location information, which may potentially threaten users' location privacy. In view of this challenge, we propose a two-tier schema for the privacy preservation based on k-anonymity principle meanwhile reducing the cost for privacy protection. Concretely, we divide the users into groups in order to maximize the privacy level and in each group one proxy is selected to generate dummy locations and share the returned results from LBS provider; then, on each group, an auction mechanism is proposed to determine the payment of each user to the proxy as the compensation, which satisfies budget balance and incentive compatibility. To evaluate the performance of the proposed schema, a simulated experiment is conducted.
Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine

The approach of dynamical system (DS) is promising for modeling robot motion, and provides a flexible means of realizing robot learning and control. Accuracy, stability, and learning speed are the three main factors to be considered when learning robot movements from human demonstrations with DS. Some approaches yield stable dynamical systems, but these may result in a poor reproduction performance, while some approaches yield good reproduction performance but are quite complex and time-consuming. In this paper, we address the accuracy-stability-speed issues simultaneously. We present a learning method named the fast and stable modeling for dynamical systems, which is based on the extreme learning machine to efficiently and accurately learn the parameters of the DS as well as to ensure the asymptotic stability at the target. We confirm the proposed approach by performing both 2-D tasks of learning handwriting motions and a set of robot experiments.
Listening to the Crowd for the Release Planning of Mobile Apps

The market for mobile apps is getting bigger and bigger, and it is expected to be worth over 100 Billion dollars in 2020. To have a chance to succeed in such a competitive environment, developers need to build and maintain high-quality apps, continuously astonishing their users with the coolest new features. Mobile app marketplaces allow users to release reviews. Despite reviews are aimed at recommending apps among users, they also contain precious information for developers, reporting bugs and suggesting new features. To exploit such a source of information, developers are supposed to manually read user reviews, something not doable when hundreds of them are collected per day. To help developers dealing with such a task, we developed CLAP (Crowd Listener for releAse Planning), a web application able to (i) categorize user reviews based on the information they carry out, (ii) cluster together related reviews, and (iii) prioritize the clusters of reviews to be implemented when planning the subsequent app release. We evaluated all the steps behind CLAP, showing its high accuracy in categorizing and clustering reviews and the meaningfulness of the recommended prioritizations. Also, given the availability of CLAP as a working tool, we assessed its applicability in industrial environments.
Dynamic Multi-Phrase Ranked Search over Encrypted Data with Symmetric Searchable Encryption

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.
Mapreduce performance model for Hadoop 2.x

MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that may provide reasonably accurate job response time estimation at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance model for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, they could not be applied to Hadoop 2.x due to fundamental architectural changes and dynamic resource allocation in Hadoop 2.x. Thus, the proposed solution is based on an existing performance model for Hadoop 1.x, but taking into consideration architectural changes and capturing the execution flow of a MapReduce job by using queuing network model. This way, the cost model reflects the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup.
${R}_1$ -2-DPCA and Face Recognition

2-D principal component analysis (2-DPCA) is one of the successful dimensionality reduction approaches for image classification and representation. However, 2-DPCA is not robust to outliers. To tackle this problem, we present an efficient robust method, namely R 1 -2-DPCA for feature extraction. R 1 -2-DPCA aims to seek the projection matrix such that the projected data have the maximum variance, which is measured by R 1 -norm. Compared with most existing robust 2-DPCA methods, our model is not only robust to outliers but also helps encode discriminant information. Accordingly, we develop a nongreedy iterative algorithm, which has not only a closed-form solution in each iteration but also a good convergence, to solve our model. Moreover, to further improve classification performance, we employ nuclear norm as the distance metric in the classification phase. Extensive experiments on several face databases illustrate that our proposed method is superior to most existing robust 2-DPCA methods.
Universal discriminative quantum neural networks

Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by non-orthogonal quantum probability distributions using the Adam stochastic optimization algorithm. This is achieved by iterative interactions of a classical device with a quantum processor to discover the parameters of an unknown non-unitary quantum circuit. This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs. Notably we use universal circuit topologies, with a theoretically motivated circuit design, which guarantees that our circuits can in principle learn to perform arbitrary input-output mappings. Our numerical simulations show that shallow quantum circuits could be trained to discriminate among various pure and mixed quantum states exhibiting a trade-off between minimizing erroneous and inconclusive outcomes with comparable performance to theoretically optimal POVMs. We train the circuit on different classes of quantum data and evaluate the generalization error on unseen mixed quantum states. This generalization power hence distinguishes our work from standard circuit optimization and provides an example of quantum machine learning for a task that has inherently no classical analogue.
Color Image Encryption Using Pixel Scrambling Operator and Reality-Preserving MPFRHT

To ensure the confidentiality of color images during their storage or transmission on insecure networks, a number of encryption methods based on fractional transforms have been proposed and widely investigated. However, most of their outputs are complex values that are inconvenient for record and transmission. Also, those methods always deal with a whole color component with the same fractional-order and ignore the textural features that are contained in different image parts. In this paper, we first define a reality-preserving multiple-parameter fractional Hartley transform (RPMPFRHT), whose output is real value, and then a novel color image encryption method is proposed that divides the RGB components into different blocks and uses a constructed pixel scrambling operator to mix and hide the original color information. The outputs are transformed to different RPMPFRHT domains and further scrambled by a non-adjacent coupled map lattices system. Numerical simulations are performed to demonstrate that the proposed encryption algorithm is feasible, secure, sensitive to keys, and robust to potential attacks.
Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field

Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.
HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain

This article proposes a simplistic algorithmic framework, namely hyperSPAM, composed of three search algorithms for addressing continuous optimisation problems. The Covariance Matrix Adaptation Evolution Strategy (CMAES) is activated at the beginning of the optimisation process as a preprocessing component for a limited budget. Subsequently, the produced solution is fed to the other two single-solution search algorithms. The first performs moves along the axes while the second makes use of a matrix orthogonalization to perform diagonal moves. Four coordination strategies, in the fashion of hyperheuristics, have been used to coordinate the two single-solution algorithms. One of them is a simple randomized criterion while the other three are based on a success based reward mechanism. The four implementations of the hyperSPAM framework have been tested and compared against each other and modern metaheuristics on an extensive set of problems including theoretical functions and real-world engineering problems. Numerical results show that the different versions of the framework display broadly a similar performance. One of the reward schemes appears to be marginally better than the others. The simplistic random coordination also displays a very good performance. All the implementations of hyperSPAM significantly outperform the other algorithms used for comparison.
Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition

In this paper, we investigate the problem of RGB-D egocentric action recognition. Unlike conventional human action videos that are passively recorded by static cameras, egocentric videos are self-generated from wearable sensors that are more flexible and provide the close-ups with the visual attention of the wearers when they act. Moreover, RGB-D videos contain the spatial appearance and temporal information in the RGB modality and reflect the 3D structure of the scenes in the depth modality. To adequately learn the nonlinear structure of heterogeneous representations from different modalities and exploit their complementary characteristics, we develop a multi-stream deep neural networks (MDNN) method, which aims to preserve the distinctive property for each modality and simultaneously explore their sharable information in a unified deep architecture. Specifically, we deploy a Cauchy estimator to maximize the correlations of the sharable components and enforce the orthogonality constraints on the distinctive components to guarantee their high independencies. Since the egocentric action recognition is usually sensitive to hand poses, we extend our MDNN by integrating with the hand cues to enhance the recognition accuracy. Extensive experimental results on a newly collected data set and two additional benchmarks are presented to demonstrate the effectiveness of our proposed method for RGB-D egocentric action recognition.
Risum teneatis, amici?☆: The socio-pragmatics of RoastMe humour

This paper gives the first academic description of RoastMe – a prevalent, but hitherto unexplored, humorous practice formed on Reddit and re-posted on various social media, notably Imgur – in the light of the relevant socio-pragmatic literature on humour and (im)politeness. Taking into account the emic perspective of the RoastMe community of practice (as opposed to recurrent etic “cyberbullying” metapragmatic evaluations), RoastMe is conceptualised as an online humorous activity which, similar to the traditional roast (including the celebrity roast genre), is centred on ritual insults addressed to the target who has agreed to be roasted for the sake of humour. RoastMe is enclosed within a humorous frame (regardless of whether or not insults are truthful, i.e. communicate roasters' true beliefs) and thrives on its community members' unbounded creativity. Roasting comments can also be seen as mock impoliteness, constituting utterances that display overtly pretended face-threat, while being oriented towards amusement and solidarity-building among anonymous users. However, even though the online community of practice conceives RoastMe as an innocuous activity, the possibility of roastees' taking, while not necessarily claiming, offence cannot be ruled out.
On the use of DAG-CNN architecture for age estimation with multi-stage features fusion

Accurate facial age estimation is quite challenging, since ageing process is dependent on gender, ethnicity, lifestyle and many other factors, therefore actual age and apparent age can be quite different. In this paper, we propose a new architecture of deep neural networks namely Directed Acyclic Graph Convolutional Neural Networks (DAG-CNNs) for age estimation which exploits multi-stage features from different layers of a CNN. Two instants of this system are constructed by adding multi-scale output connections to the underlying backbone from two well-known deep learning architectures, namely VGG-16 and GoogLeNet. DAG-CNNs not only fuse the feature extraction and classification stages of the age estimation into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Fine-tuning such models helps to increase the performance and we show that even "off-the-shelf" multi-scale features perform quite well. Experiments on the publicly available Morph-II and FG-NET databases prove the effectiveness of our novel method.
Power Normalizing Second-Order Similarity Network for Few-Shot Learning

Second-and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained image and scene recognition. However, these statistics need to be passed via an appropriate pooling scheme to obtain the best performance. Power Normalizations are non-linear activation units which enjoy probability-inspired derivations and can be applied in CNNs. In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations. To this end, we propose several formulations capturing second-order statistics and derive a sigmoid-like Power Normalizing function to demonstrate its interpretability. Our model is trained end-to-end to learn the similarity between the support set and query images for the problem of one-and few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC datasets demonstrate that this network obtains state-of-the-art results on several few-shot learning protocols.
Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention

Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity.We evaluated the use of phase-amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method.Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task.Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.
Vector-kernel convolutional neural networks

In computer vision, convolutional neural networks (CNNs) obtain extremely striking recognition performance. However, in many CNNs there exists a great deal of parameter redundancy because of matrix kernels. To address this problem, we propose a novel model, namely, vector-kernel convolutional neural network (VeckerNet). In a VeckerNet, each convolutional layer can only use vector kernels of either size k × 1 or 1 × k. Compared to the popular models, e.g., AlexNet, VGG, ResNet and DenseNet, the VeckerNets obtain up to 20.8% relative performance improvement with the parameter reduction by 3 to 97%. Impressively, compared to the ResNets with the same depth, e.g., 44, 56, 101 and 110 layers, the VeckerNets obtain 0.57 to 1.4% relative performance improvement with a decrease of parameters by up to more than two-thirds. The experimental results indicate that the VeckerNets can retain good recognition performance while effectively reducing network parameters.