Reviewerly/exHarmony huggingface dataset:
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

Federated learning (FL) and split neural networks (SplitNN) are state-of-art distributed machine learning techniques to enable machine learning without directly accessing raw data on clients or end devices. In theory, such distributed machine learning techniques have great potential in distributed applications, in which data are typically generated and collected at the client-side while the collected data should be processed by the application deployed at the server-side. However, there is still a significant gap in evaluating the performance of those techniques concerning their practicality in the Internet of Things (IoT)-enabled distributed systems constituted by resource-constrained devices. This work is the first attempt to provide empirical comparisons of FL and SplitNN in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For the learning performance (i.e., model accuracy and convergence time), we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. For implementation overhead, we mount both FL and SplitNN on Raspberry Pi devices and comprehensively evaluate their overhead, including training time, communication overhead, power consumption, and memory usage. Our key observations are that under the IoT scenario where the communication traffic is the primary concern, FL appears to perform better over SplitNN because FL has a significantly lower communication overhead compared with SplitNN. However, our experimental results also demonstrate that neither FL or SplitNN can be applied to a heavy model, e.g., with several million parameters, on resource-constrained IoT devices because its training cost would be too expensive for such devices. Source code is released and available: https://github.com/Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi.
What Makes an Online Review More Helpful: An Interpretation Framework Using XGBoost and SHAP Values

Online product reviews play important roles in the word-of-mouth marketing of e-commerce enterprises, but only helpful reviews actually influence customers’ purchase decisions. Current research focuses on how to predict the helpfulness of a review but lacks a thorough analysis of why it is helpful. In this paper, feature sets covering review text and context cues are firstly proposed to represent review helpfulness. Then, a set of gradient boosted trees (GBT) models is introduced, and the optimal one, which as implemented in eXtreme Gradient Boosting (XGBoost), is chosen to predict and explain review helpfulness. Specially, by including the SHAP (Shapley) values method to quantify feature contribution, this paper presents an integrated framework to better interpret why a review is helpful at both the macro and micro levels. Based on real data from Amazon.cn, this paper reveals that the number of words contributes the most to the helpfulness of reviews on headsets and is interactively influenced by features like the number of sentences or feature frequency, while feature frequency contributes the most to the helpfulness of facial cleanser reviews and is interactively influenced by the number of adjectives used in the review or the review’s entropy. Both datasets show that individual feature contributions vary from review to review, and individual joint contributions gradually decrease with the increase of feature values.
Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing

Automatic classification of breast cancer histopathological images is of great application value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights semantics, while capsule network (CapsNet) focuses on detailed information about the position and posture. Combining these information can obtain more discriminative features which is useful to improve the classification performance. In the paper, breast cancer histopathological image classification based on deep feature fusion and enhanced routing (FE-BkCapsNet) is proposed to take advantages of CNN and CapsNet. First, a novel structure with dual channels which can extract convolution features and capsule features simultaneously, integrate sematic features and spatial features into new capsules to obtain more discriminative information is designed. Then, routing coefficients are optimized indirectly and adaptively by modifying the loss function and embedding the routing process into entire optimization process. The proposed method FE-BkCapsNet was tested on a public dataset BreaKHis. Experimental results (40×: 92.71%, 100×: 94.52%, 200×: 94.03%, 400×: 93.54) demonstrate that the proposed method is efficient for breast cancer classification in clinical settings.
Clustering-based anomaly detection in multivariate time series data

Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal and variable relationships. In this paper, we propose a clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series. First, we use a sliding window to generate a set of multivariate subsequences and thereafter apply an extended fuzzy clustering to reveal a structure present within the generated multivariate subsequences. Finally, a reconstruction criterion is employed to reconstruct the multivariate subsequences with the optimal cluster centers and the partition matrix. We construct a confidence index to quantify a level of anomaly detected in the series and apply Particle Swarm Optimization as an optimization vehicle for the problem of anomaly detection. Experimental studies completed on several synthetic and six real-world datasets suggest that the proposed methods can detect the anomalies in multivariate time series. With the help of available clusters revealed by the extended fuzzy clustering, the proposed framework can detect anomalies in the multivariate time series and is suitable for identifying anomalous amplitude and shape patterns in various application domains such as health care, weather data analysis, finance, and disease outbreak detection.
CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices

Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computationally intensive Deep Neural Networks (DNNs) on resource-constrained edge devices, traditional approaches have relied on either offloading workload to the remote cloud or optimizing computation at the end device locally. However, the cloud-assisted approaches suffer from the unreliable and delay-significant wide-area network, and the local computing approaches are limited by the constrained computing capability. Towards high-performance edge intelligence, the cooperative execution mechanism offers a new paradigm, which has attracted growing research interest recently. In this paper, we propose CoEdge, a distributed DNN computing system that orchestrates cooperative DNN inference over heterogeneous edge devices. CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions. Experimental evaluations based on a realistic prototype show that CoEdge outperforms status-quo approaches in saving energy with close inference latency, achieving up to 25.5% ~ 66.9% energy reduction for four widely-adopted CNN models.
Blockchain and energy: A bibliometric analysis and review

Blockchain technology provides an immutable ledger for secure value transactions in a network. This base layer technology has the potential to boost the efficiency of various processes in the energy sector. In this article, the intersection of blockchain and energy is analyzed based on the underlying references of 166 publications via co-citation analysis. Using exploratory factor analysis, six distinct research streams are identified: I. energy market innovation and transformation (through blockchain technology), II. blockchain for data sharing and security, III. energy management in smart grids and scalable systems, IV. information transmission across networks and its applications, V. peer-to-peer energy microgrids, and VI. potential of blockchain technology. For each of these streams, the highest-impact articles are reviewed. In addition, social network analysis allows to reveal the relationships and dependencies between the streams. The results indicate a high degree of homogeneity in this field of research, as the six streams explain more than 71% of variance. The degree to which the streams are centered on blockchain technology varies. While the two most established discourses and the least established one focus at least in part on blockchain, the other three streams prioritize energy issues. It is postulated that specific research fields on this topic are only beginning to emerge, implications are discussed and areas for future research are derived.
Agile, Traditional, and Hybrid Approaches to Project Success: Is Hybrid a Poor Second Choice?

Three project management approaches—traditional, agile, and hybrid—were considered in this study. Results from an international study, including 477 cross-industry projects, indicated that 52% of projects could be categorized as hybrid approaches. A regression analysis using multiple outcome measures indicated substantial explanatory power (0.21 &lt; R 2 &lt;0.41). Analysis suggested that hybrid and agile approaches significantly increase stakeholder success over traditional approaches while achieving the same budget, time, scope, and quality outcomes. Hybrid approaches were found to be similar in effectiveness to fully agile approaches. Results validate decisions by practitioners to combine agile and traditional practices and suggest that hybrid is a leading project management approach.
Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.
From federated learning to federated neural architecture search: a survey

Abstract Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.
Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

Despite the numerous and noticeable inherited gains of Mobile Cloud Computing (MCC) in healthcare, its growth is being hindered by privacy and security challenges. Such issues require the utmost urgent attention to realize its full scale and efficient usage. There is a need to secure Health Information worldwide, regionally, and locally. To fully avail of the health services, it is crucial to put in place the demanded security practices for the prevention of security breaches and vulnerabilities. Hence, this research is deliberated on to provide requirement-oriented health information security using the Modular Encryption Standard (MES) based on the layered modeling of the security measures. The performance analysis shows that the proposed work excels, compared to other commonly used algorithms against the health information security at the MCC environment in terms of better performance and auxiliary qualitative security ensuring measures.
Agile versus Waterfall Project Management: Decision Model for Selecting the Appropriate Approach to a Project

Procedural models for project management can be differentiated into plan-driven methods which follow a classical waterfall process and agile methods which follow an iterative, test-driven approach. This paper answers the following research questions: What are the differences, benefits, and constraints of these two approaches from a practitioner’s point of view? How can a decision model be set up to select the most appropriate approach for a concrete project? This study develops a decision model for the selection of a procedural model for project management which is based on the modelling process described by Adam (1996). The research gap was identified based on a systematic and comprehensive analysis of the literature following Vom Brocke et al. (2009), which reflects the state-of-research. Insights gained were compared with empirical data from 15 expert interviews across different industries in Germany. The presented model systematically supports the selection of an appropriate procedural model for a concrete project based on 15 criteria subsumed under the following categories: scope, time, costs, organization context, and project-team characteristics. It closes a relevant research gap, both from a scientific perspective and from the practitioners’ view. Expert interviews ensure practical relevance and significantly expand the state-of-research with regards to decision support on the project-management approach.
Convergence of the RMSProp deep learning method with penalty for nonconvex optimization

A norm version of the RMSProp algorithm with penalty (termed RMSPropW) is introduced into the deep learning framework and its convergence is addressed both analytically and numerically. For rigour, we consider the general nonconvex setting and prove the boundedness and convergence of the RMSPropW method in both deterministic and stochastic cases. This equips us with strict upper bounds on both the moving average squared norm of the gradient and the norm of weight parameters throughout the learning process, owing to the penalty term within the proposed cost function. In the deterministic (batch) case, the boundedness of the moving average squared norm of the gradient is employed to prove that the gradient sequence converges to zero when using a fixed step size, while with diminishing stepsizes, the minimum of the gradient sequence converges to zero. In the stochastic case, due to the boundedness of the weight evolution sequence, it is further shown that the weight sequence converges to a stationary point with probability 1. Finally, illustrative simulations are provided to support the theoretical analysis, including a comparison with the standard RMSProp on MNIST, CIFAR-10, and IMDB datasets.
Experimental Realization of a Protected Superconducting Circuit Derived from the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mn>0</mml:mn></mml:math> – <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>π</mml:mi></mml:math> Qubit

Encoding a qubit in logical quantum states with wavefunctions characterized by disjoint support and robust energies can offer simultaneous protection against relaxation and pure dephasing. Using a circuit-quantum-electrodynamics architecture, we experimentally realize a superconducting $0-\pi$ qubit, which hosts protected states suitable for quantum-information processing. Multi-tone spectroscopy measurements reveal the energy level structure of the system, which can be precisely described by a simple two-mode Hamiltonian. We find that the parity symmetry of the qubit results in charge-insensitive levels connecting the protected states, allowing for logical operations. The measured relaxation (1.6 ms) and dephasing times (25 $\mu$s) demonstrate that our implementation of the $0-\pi$ circuit not only broadens the family of superconducting qubits, but also represents a promising candidate for the building block of a fault-tolerant quantum processor.
Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller

This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply considered as a combination of a fuzzy neural network (FNN) and a radial basis function neural network (RBF NN) to improve the accuracy of a nonlinear approximation, so it has the advantages of these two neural networks. The main advantage of the proposed new FDHLRNN is that the output values of the FNN and DHLRNN are considered at the same time, and the outer layer feedback is added to increase the dynamic approximation ability. FDHLRNN was designed to approximate the nonlinear sliding-mode equivalent control term to reduce the switching gain. To ensure the best approximation capability and control performance, the proposed FDHLRNN using TSMC is applied for the second-order nonlinear model. Two simulation examples are implemented to verify that the proposed FDHLRNN has faster convergence speed and the FDHLRNN with TSMC has good dynamic property and robustness, and a hardware experimental study with an active power filter proves the feasibility of the method.
A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

In recent times, we can see a massive increase in the number of devices that are being connected to the internet. These devices include but are not limited to smartphones, IoT, and cloud networks. In comparison to other possible cyber-attacks, these days, hackers are targeting these devices with phishing attacks since it exploits human vulnerabilities rather than system vulnerabilities. In a phishing attack, an online user is deceived by a seemingly trusted entity to give their personal data, i.e., login credentials or credit card details. When this private information is leaked to the hackers, this information becomes the source of other sophisticated attacks. In recent times many researchers have proposed the machine learning-based approach to solve phishing attacks; however, they have used a large number of features to develop reliable phishing detection techniques. A large number of features requires large processing powers to detect phishing, which makes it very much unsuitable for resource constrained devices. To address this issue, we have developed a phishing detection approach that only needs nine lexical features for effectively detecting phishing attacks. We used ISCXURL-2016 dataset for our experimental purpose, where 11964 instances of legitimate and phishing URLs are used. We have tested our approach against different machine learning classifiers and have obtained the highest accuracy of 99.57% with the Random forest algorithm.
Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.