Reviewerly/exHarmony huggingface dataset:
Study on enhancing AIoT computational thinking skills by plot image-based VR

Due to their applications on varied and complex issues, Artificial Intelligence (AI) and Internet of Things (IoT) (collectively, AIoT) have become popular new-generation courses, but the learning of such courses needs to consider actual situations and to analyze complicated problems, making it difficult for students to improve their academic performance. The concept of computational thinking education is to allow students to find solutions to problems in different fields by understanding the problems and analyzing solution steps. In this study, we integrate the concept of computational thinking education into a course on plot image-based virtual reality (VR). It combined shooting of the actual field, instructions on the movie required to be shot, and VR in order to enable students to get immersed in thinking about problems and understanding the actual field through plot image-based VR, further plan to learn the essence of problems, and develop possible solutions. Through experimental results of experimental and control groups, we found that plot image-based VR can effectively improve students' interests in learning and computational thinking.
Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study

Code smells or bad smells are an accepted approach to identify design flaws in the source code. Although it has been explored by researchers, the interpretation of programmers is rather subjective. One way to deal with this subjectivity is to use machine learning techniques. This paper provides the reader with an overview of machine learning techniques and code smells found in the literature, aiming at determining which methods and practices are used when applying machine learning for code smells identification and which machine learning techniques have been used for code smells identification. A mapping study was used to identify the techniques used for each smell. We found that the Bloaters was the main kind of smell studied, addressed by 35% of the papers. The most commonly used technique was Genetic Algorithms (GA), used by 22.22% of the papers. Regarding the smells addressed by each technique, there was a high level of redundancy, in a way that the smells are covered by a wide range of algorithms. Nevertheless, Feature Envy stood out, being targeted by 63% of the techniques. When it comes to performance, the best average was provided by Decision Tree, followed by Random Forest, Semi-supervised and Support Vector Machine Classifier techniques. 5 out of the 25 analyzed smells were not handled by any machine learning techniques. Most of them focus on several code smells and in general there is no outperforming technique, except for a few specific smells. We also found a lack of comparable results due to the heterogeneity of the data sources and of the provided results. We recommend the pursuit of further empirical studies to assess the performance of these techniques in a standardized dataset to improve the comparison reliability and replicability.
Intelligent sentiment analysis approach using edge computing‐based deep learning technique

Summary Sentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.
Classification of Breast Cancer Histology Images Using Transfer Learning

Breast Cancer is a most common form of cancer among women and life taking disease around the globe. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. This is a tiresome task and for that reason, Deep Neural Networks are being used for the supervised classification. We have used Breast Histology dataset having 240 training and 20 test images for classification of the histology images among four classes, i.e. Normal, Benign, In-situ carcinoma and Invasive carcinoma. The dataset was preprocessed for proper classification. We have applied transfer learning based on AlexNet, GoogleNet, and ResNet that can classify images at multiple cellular and nuclei configurations. This approach has resulted in 85% accuracy in case of ResNet as the highest among others and further research is being done to increase its efficiency and reduce the human dependency. The proposed design can also be enhanced for automation of other medical imaging methods.
Development of a Markov-Chain-Based Solar Generation Model for Smart Microgrid Energy Management System

The growth of smart power grids has complicated the balancing of supply and demand, the control and management of power outages, and the reduction of grid costs. In order to strengthen power grid reliability and reduce the risk of failure in production resources, demand-side management has gained particular importance. Microgrids are dramatically expanding due to the potential benefits of an increasingly reliable, sustainable, efficient, and environmentally friendly energy supply from renewable energy sources. In recent years, the air pollution crisis has attracted much attention. Among the factors that aggravate this crisis, pollution from fossil fuels has been playing a significantly greater role. As a result, the trend toward renewable energies, such as solar power, has grown considerably. As a gradually introduced alternative to fossil fuels, solar energy presents many benefits as well as numerous challenges. One of the most critical issues is the unreliability of this energy source. The management solution is to define an appropriate model for predicting how solar energy works and how to accordingly manage its storage. This paper proposes a solar generation model based on the Markov Chain, by which the formation of a cluster of chains predicts the power generation of solar cells. In order to reduce the prediction error, Markov chains are continuously trained by historical data. Evaluation results confirm the accuracy of the proposed model.
DLattice: A Permission-Less Blockchain Based on DPoS-BA-DAG Consensus for Data Tokenization

In today's digital information age, the conflict between the public's growing awareness of their own data protection and the data owners' inability to obtain data ownership has become increasingly prominent.The emergence of blockchain provides a new direction for data protection and data tokenization.Nonetheless, existing cryptocurrencies such as Bitcoin using Proof-of-Work are particularly energy intensive.On the other hand, classical protocols such as Byzantine agreement do not work efficiently in an open environment.Therefore, in this paper, we propose a permission-less blockchain with a novel double-DAG (directed acyclic graph) architecture called DLattice, where each account has its own Account-DAG and all accounts make up a greater Node-DAG structure.DLattice parallelizes the growth of each account's Account-DAG, each of which is not influenced by other accounts' irrelevant transactions.DLattice uses a new DPoS-BA-DAG(PANDA) protocol to reach consensus among users only when the forks are observed.Based on proposed DLattice, we introduce a process of data tokenization, including data assembling, data anchoring, and data authorization.We implement DLattice and evaluate its performance on 25 ECS virtual machines, simulating up to 500 nodes.The experimental results show that DLattice reaches a consensus in 10 seconds, achieves desired throughput, and incurs almost no penalty for scaling to more users.
Genuinely entangled subspace with all-encompassing distillable entanglement across every bipartition

In a multipartite scenario quantum entanglement manifests its most dramatic form when the state is genuinely entangled. Such a state is more beneficial for information theoretic applications if it contains distillable entanglement in every bipartition. It is, therefore, of significant operational interest to identify subspaces of multipartite quantum systems that contain such properties apriori. In this letter, we introduce the notion of unextendible biseparable bases (UBB) that provides an adequate method to construct genuinely entangled subspaces (GES). We provide an explicit construction of two types of UBBs -- party symmetric and party asymmetric -- for every $3$-{\it qudit} quantum system, with local dimension d\ge 3. Further, we show that the GES resulting from the symmetric construction is indeed a {\it bidistillable} subspace, i.e., all the states supported on it contain distillable entanglement across every bipartition.
The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses

Most recommender systems are evaluated on how they accurately predict user ratings. However, individuals use them for more than an anticipation of their preferences. The literature demonstrated that some recommendation algorithms achieve good prediction accuracy, but suffer from popularity bias. Other algorithms generate an item category bias due to unbalanced rating distributions across categories. These effects have been widely analyzed in the context of books, movies, music, and tourism, but contrasting conclusions have been reached so far. In this paper, we explore how recommender systems work in the context of massive open online courses, going beyond prediction accuracy. To this end, we compared existing algorithms and their recommended lists against biases related to course popularity, catalog coverage, and course category popularity. Our study remarks even more the need of better understanding how recommenders react against bias in diverse contexts.
PSO-based Minimum-time Motion Planning for Multiple Vehicles Under Acceleration and Velocity Limitations

This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature curvesthree-degree Bezier curvesis selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity profile generation using the local minimum of the radial-accelerated linear velocity profile, which reduces the calculation effort, is introduced. A new PSO-based search algorithm, called “particle-group-based PSO,” is introduced to find the best combination of trajectories that minimizes the traveling time of the slowest vehicle. A particle group is designed to wrap a set of particles representing each vehicle. The first and last two control points characterizing a curve are used as the state vector of a particle. Simulation results demonstrating the performance of the proposed method are presented. The main advantage of the proposed method is its minimization of the velocity-profile-generation time, and thereby, its maximization of the search time.
GlotNet—A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis

Recently, generative neural network models which operate directly on raw audio, such as WaveNet, have improved the state of the art in text-to-speech synthesis (TTS). Moreover, there is increasing interest in using these models as statistical vocoders for generating speech waveforms from various acoustic features. However, there is also a need to reduce the model complexity, without compromising the synthesis quality. Previously, glottal pulseforms (i.e., time-domain waveforms corresponding to the source of human voice production mechanism) have been successfully synthesized in TTS by glottal vocoders using straightforward deep feedforward neural networks. Therefore, it is natural to extend the glottal waveform modeling domain to use the more powerful WaveNet-like architecture. Furthermore, due to their inherent simplicity, glottal excitation waveforms permit scaling down the waveform generator architecture. In this study, we present a raw waveform glottal excitation model, called GlotNet, and compare its performance with the corresponding direct speech waveform model, WaveNet, using equivalent architectures. The models are evaluated as part of a statistical parametric TTS system. Listening test results show that both approaches are rated highly in voice similarity to the target speaker, and obtain similar quality ratings with large models. Furthermore, when the model size is reduced, the quality degradation is less severe for GlotNet.
Blockchain based privacy-preserving software updates with proof-of-delivery for Internet of Things

A large number of IoT devices are connected via the Internet. However, most of these IoT devices are generally not perfect-by-design even have security weaknesses or vulnerabilities. Thus, it is essential to update these IoT devices securely, patching their vulnerabilities and protecting the safety of the involved users. Existing studies deliver secure and reliable updates based on blockchain network which serves as the transmission network. However, these approaches could compromise users privacy when updating the IoT devices. In this paper, we propose a new blockchain based privacy-preserving software update protocol, which delivers secure and reliable updates with an incentive mechanism while protects the privacy of involved users. A vendor delivers the updates and makes a commitment by using smart contract to provide financial incentive to the transmission nodes who deliver the updates to its IoT devices. A transmission node can get financial incentive by providing a proof-of-delivery. In order to obtain the proof-of-delivery, the transmission node uses double authentication preventing signature (DAPS) to carry out fair exchange. Specifically, the transmission node uses the DAPS to exchange an attribute-based signature (ABS) of one IoT device. Then, it uses the ABS as proof-of-delivery to receive financial incentives. Generally, to generate an ABS, the IoT device has to execute complex computations which is intolerable for resource limited devices. We propose a concrete outsourced attribute-based signature (OABS) scheme to overcome the weakness. Then, we prove the security of the proposed OABS and the protocol. Finally, we implement smart contract in Solidity to demonstrate the validity of the proposed protocol.
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection

The amount of time series data generated in Healthcare is growing very fast and so is the need for methods that can analyse these data, detect anomalies and provide meaningful insights. However, most of the data available is unlabelled and, therefore, anomaly detection in this scenario has been a great challenge for researchers and practitioners. Recently, unsupervised representation learning with deep generative models has been applied to find representations of data, without the need for big labelled datasets. Motivated by their success, we propose an unsupervised framework for anomaly detection in time series data. In our method, both representation learning and anomaly detection are fully unsupervised. In addition, the training data may contain anomalous data. We first learn representations of time series using a Variational Recurrent Autoencoder. Afterwards, based on those representations, we detect anomalous time series using Clustering and the Wasserstein distance. Our results on the publicly available ECG5000 electrocardiogram dataset show the ability of the proposed approach to detect anomalous heartbeats in a fully unsupervised fashion, while providing structured and expressive data representations. Furthermore, our approach outperforms previous supervised and unsupervised methods on this dataset.
Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e.one spectral dimension and two spatial position dimensions. Multispectral image compression canbe achieved by means of the advantages of tensor decomposition (TD), such as NonnegativeTucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity andcannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardwareresources and power are limited. Here, we propose a low-complexity compression approach formultispectral images based on convolution neural networks (CNNs) with NTD. We construct anew spectral transform using CNNs, where the CNNs are able to transform the three-dimensionspectral tensor from large-scale to a small-scale version. The NTD resources only allocate thesmall-scale three-dimension tensor to improve calculation efficiency. We obtain the optimizedsmall-scale spectral tensor by the minimization of original and reconstructed three-dimensionspectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimensionspectral tensor in the DCT domain to obtain the high compression performance. We experimentallyconfirmed the proposed method on multispectral images. Compared to the case that the newspectral tensor transform with CNNs is not applied to the original three-dimension spectral tensorat the same compression bit-rates, the reconstructed image quality could be improved. Comparedwith the full NTD-based method, the computation efficiency was obviously improved with only asmall sacrifices of PSNR without affecting the quality of images.
Dynamics of a higher dimensional fractional-order chaotic map

Fractional calculus has been a hot topic in nonlinear science, because it can describe physical phenomena more accurately. In recent years, the theory of fractional difference has become one of the foundations for fractional-order chaotic maps. Based on this concept, a higher dimensional fractional-order chaotic map is investigated. Different from other proposed fractional-order chaotic maps, it has multiple dimensions, and can be set as incommensurate fractional order. The bifurcation diagrams and attractors of this system with changing fractional orders are plotted by numerical simulations, and the maximum Lyapunov exponent (MLE), the permutation entropy complexity, and the probability density distribution are also calculated. Results show it has rich dynamical behaviors. Many of the periodic windows in the integer-order map become chaos when they are in the fractional-order form. And the complexity of the fractional-order form is higher than that of its integer-order counterpart and other nine typical chaotic maps. In addition, the distribution of fractional-order system is also better than that of the integer-order one. These results lay the theoretical foundation for its practical applications and relevant to the study of other fractional-order chaotic maps.
PortraitNet: Real-time portrait segmentation network for mobile device

Real-time portrait segmentation plays a significant role in many applications on mobile device, such as background replacement in video chat or teleconference. In this paper, we propose a real-time portrait segmentation model, called PortraitNet, that can run effectively and efficiently on mobile device. PortraitNet is based on a lightweight U-shape architecture with two auxiliary losses at the training stage, while no additional cost is required at the testing stage for portrait inference. The two auxiliary losses are boundary loss and consistency constraint loss. The former improves the accuracy of boundary pixels, and the latter enhances the robustness in complex lighting environment. We evaluate PortraitNet on portrait segmentation dataset EG1800 and Supervise-Portrait. Compared with the state-of-the-art methods, our approach achieves remarkable performance in terms of both accuracy and efficiency, especially for generating results with sharper boundaries and under severe illumination conditions. Meanwhile, PortraitNet is capable of processing 224 × 224 RGB images at 30 FPS on iPhone 7.
Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic

As stalling is the worst Quality of Experience (QoE) degradation of HTTP adaptive video streaming (HAS), this work presents a stream-based machine learning approach, ViCrypt, which analyzes stalling of YouTube streaming sessions in realtime from encrypted network traffic. The video streaming session is subdivided into a stream of short time slots of 1s length, while considering two additional macro windows each for the current streaming trend and the whole ongoing streaming session. Constant memory features are extracted from the encrypted network traffic in these three windows in a stream-based fashion, and fed into a random forest model, which predicts whether the current time slot contains stalling or not. The presented system can predict stalling with a very high accuracy and the finest granularity to date (1s), and thus, can be used in networks for real-time QoE analysis from encrypted YouTube video streaming traffic. The independent predictions for each consecutive slot of a streaming session can further be aggregated to obtain stalling estimations for the whole session. Thereby, the proposed method allows to quantify the initial delay, as well as the overall number of stalling events and the stalling ratio, i.e., the ratio of total stalling time and total playback time.
Variant of greedy randomized Kaczmarz for ridge regression

The variants of randomized Kaczmarz (RK) and randomized Gauss-Seidel (RGS) are distinct iterative algorithms for ridge regression. Theoretical convergence rates for these two algorithms were provided by a simple side-by-side analysis in recent work. Considering that greedy randomized Kaczmarz (GRK) algorithm converges faster than the RK algorithm in both theory and experiments, we extend the GRK algorithm for ordinary least squares (OLS) regression to ridge regression and establish the corresponding convergence theory for the resulting algorithm and, furthermore, we study the GRK algorithm with relaxation for ridge regression and prove that it also converges with expected exponential rate in this paper. In addition, we propose an accelerated GRK algorithm with relaxation for ridge regression by executing more rows that corresponding to the larger entries of the residual vector simultaneously at each iteration. Numerical results show that the GRK algorithm with and without relaxation for ridge regression perform much better in iteration steps than the variants of RK and RGS algorithms, and the accelerated GRK algorithm with relaxation significantly outperforms all other algorithms in terms of both iteration counts and computing times.