Reviewerly/exHarmony huggingface dataset:
A quantum convolutional neural network on NISQ devices

Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O (( log 2 M ) 6 ) basic gates and O ( m 2 + e ) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.
Smog prediction based on the deep belief - BP neural network model (DBN-BP)

Smog pollution is becoming a significant problem for people worldwide, becoming an essential threat to the global environment. Many studies on haze already exist, which still need to continue in-depth research to better deal with haze problems. Due to its unique geographical environment, Sichuan has become one of the areas with severe smog pollution. Therefore, the research and prediction of smog pollution in Sichuan has become an urgent need. This paper proposes a deep learning technology based on a Deep Belief-Back Propagation neural network. It makes in-depth prediction research by using the air pollution data of PM2.5, PM10, O3, CO NO2, and SO2 in Sichuan smog to provide a decision-making basis for predicting and preventing smog polluted weather. According to the prediction results of the model, the concentrations of PM2.5 and PM10 in Chengdu were predicted. The analysis shows that the larger the number of hidden layers in the belief network, the higher the prediction accuracy. Under the same network, the prediction accuracy of PM2.5 is significantly higher than that of PM10. Compared with the traditional Back Propagation neural network, the prediction effect of the Deep Belief-Back Propagation neural network is better.
Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the enhanced variants of velocity-free particle swarm optimizer with proven defects and shortcomings in performance. Regardless of the proven defect and lack of novelty in this algorithm, the GWO has a simple algorithm and it may face considerable unbalanced exploration and exploitation trends. However, GWO is easy to be utilized, and it has a low capacity to deal with multi-modal functions, and it quickly falls into the optima trap or fails to find the global optimal solution. To improve the shortcomings of the basic GWO, this paper proposes an improved GWO called multi-stage grey wolf optimizer (MGWO). By dividing the search process into three stages and using different population updating strategies at each stage, the MGWO’s optimization ability is improved while maintaining a certain convergence speed. The MGWO cannot easily fall into premature convergence and has a better ability to get rid of the local optima trap than GWO. Meanwhile, the MGWO achieves a better balance of exploration and exploitation and has a rough balance curve. Hence, the proposed MGWO can obtain a higher-quality solution. Based on verification on the thirty benchmark functions of IEEE CEC2017 as the objective functions, the simulation experiments in which MGWO compared with some swarm-based optimization algorithms and the balance and diversity analysis were conducted. The results verify the effectiveness and superiority of MGWO. Finally, the MGWO was applied to the multi-threshold image segmentation of Leaf Spot Diseases on Maize at four different threshold levels. The segmentation results were analysed by comparing each comparative algorithm’s PSNR, SSIM, and FSIM. The results proved that the MGWO has noticeable competitiveness, and it can be used as an effective optimizer for multi-threshold image segmentation.
Underwater image restoration via backscatter pixel prior and color compensation

The use of underwater cameras instead of divers to enter complex underwater areas for real-time monitoring of fish, shrimp, and algae is significant to the aquaculture industry. However, underwater images are severely degraded due to light absorption and scattering, limiting the development of underwater computer vision and robot vision perception. To solve blurriness and color degradation issues, this paper developed a restoration method based on backscatter pixel prior and color cast removal from the physical point of view of underwater image degradation. The proposed method used only a single underwater image as an input to estimate various parameters accurately, such as depth map, backscatter map, and illuminant map. Specifically, a backscatter estimation algorithm based on a depth map was proposed to improve the contrast of underwater images. Then, an algorithm was designed to remove color deviation based on the illuminant map. In particular, a color compensation strategy was created that could completely eliminate red artifacts in underwater images that were generated by the strong attenuation of the red channel. We designed comparative experiments from multiple angles on different real underwater image datasets. Experiments showed that the proposed method improved the contrast and removed the color deviation of light absorption compared to several reported methods. Even on underwater images with severe attenuation, the proposed method showed a significant positive effectiveness and stability on color cast removal.
Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist's experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur's entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.
A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems

The cloud computing scheme promises many salient features such as on-demand resource provisioning to users, and it therefore has drawn significant attention from the cyber-physical systems (CPS). An increasing number of CPS have been deployed in cloud platforms, and to accommodate numerous CPS applications, cloud datacenters often consist of a huge number of physical computation and storage nodes, and the number is still increasing. As a result, the electricity power consumption in cloud datacenters is considerable, currently accounting for about 1.3% of the worldwide electricity. How to reduce the energy consumption of datacenters is an economically beneficial but challenging problem. Optimizing virtual machine (VM) scheduling in datacenters by live VM migration is an appealing method to save energy consumption. However, it is still a challenge to conduct VM scheduling in an energy-efficient and performance-guaranteed manner, since VM migration can suffer from severe performance degradation while saving energy. In this paper, we propose a balanced VM scheduling method to achieve trade-offs between energy and performance in cyber-physical cloud systems. Specifically, the problem is formulated via a joint optimization model, and a balanced VM scheduling method is proposed accordingly to determine which VMs and where should be migrated, aiming at both reducing energy consumption and mitigating performance degradation. Both analytical and simulation results demonstrate the effectiveness and efficiency of our method.
Pixelwise Deep Sequence Learning for Moving Object Detection

Moving object detection is an essential, well-studied but still open problem in computer vision and plays a fundamental role in many applications. Traditional approaches usually reconstruct background images with hand-crafted visual features, such as color, texture, and edge. Due to lack of prior knowledge or semantic information, it is difficult to deal with complicated and rapid changing scenes. To exploit the temporal structure of the pixel-level semantic information, in this paper, we propose an end-to-end deep sequence learning architecture for moving object detection. First, the video sequences are input into a deep convolutional encoder-decoder network for extracting pixel-wise semantic features. Then, to exploit the temporal context, we propose a novel attention long short-term memory (Attention ConvLSTM) to model pixelwise changes over time. A spatial transformer network and a conditional random field layer are finally appended to reduce the sensitivity to camera motion and smooth the foreground boundaries. A multi-task loss is proposed to jointly optimization for frame-based classification and temporal prediction in an end-to-end network. Experimental results on CDnet 2014 and LASIESTA show 12.15% and 16.71% improvement to the state of the art, respectively.
Human behavior characterization for driving style recognition in vehicle system

Despite the development of new technologies in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. We consider a dataset composed by 51 different features extracted by 10 different drivers, evaluating the efficiency of the proposed method in driver identification. We also find the most relevant features able to discriminate the car owner by an impostor. We obtain a precision and a recall equal to 99% evaluating a dataset containing data extracted from real vehicle.
QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications

IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure.
Cognitive Biases in Software Engineering: A Systematic Mapping Study

One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering (SE) research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles, which investigate 37 cognitive biases, published between 1990 and 2016. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work.
Wearable Sensor Devices for Prevention and Rehabilitation in Healthcare: Swimming Exercise With Real-Time Therapist Feedback

Wearable sensor devices are playing an increasingly important role in providing pervasive and personalized healthcare. Important elements of modern healthcare services are physical rehabilitation and injury prevention. Wearable sensor devices attached to the patient can offer valuable supplemental information to healthcare professionals during treatment or therapy. In physical rehabilitation, wearable inertial sensor devices help therapists to monitor and evaluate parameters and key performance indicators of the rehabilitation activities. Therapy efficiency can be increased by using real-time feedback systems. Three different feedback system architectures are defined and studied: 1) therapist; 2) user; and 3) cloud system. A case study involving rehabilitation therapy based on swimming exercises was performed. Field test results show that the developed sensor device and real-time therapist feedback application provide sufficiently accurate and precise data for the efficient evaluation of swimming parameters, such as stroke time and stroke rotation angle symmetry. Rehabilitation therapists should be able to operate the system without the constant presence of trained and experienced technical personnel (engineers). The wide adoption of wearable sensor device-based applications will lead to cloud systems where big data analytics will offer additional benefits to healthcare, well-being, and quality of life.
Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites

Short-term forecasting of solar irradiance at a targeted site with consideration of its time series and measurements at neighboring sites is studied in this paper. A data-driven framework for forecasting solar irradiance based on fusing spatial and temporal information is proposed. In the framework, data-driven approaches including boosted regression trees (BRT), artificial neural network, support vector machine, and least absolute shrinkage and selection operator are applied to model spatial dependence among solar irradiance time series thereby to generate forecasts of solar irradiance at the targeted site. A comprehensive comparison among data-driven forecasting models is performed using 30-min averaged data of recent two years. Moreover, benchmarking models including scaled persistence model, autoregressive model, and autoregressive exogenous model are employed to further validate the effectiveness of data-driven forecasting models. Computational results of multiple-steps ahead forecasting demonstrate that the BRT model gives the best performance with the lowest normalized root mean squared error of 18.4%, 24.3%, 27.9%, and 30.6% for forecasting horizons of 30, 60, 90, and 120-min, respectively.
A two-stage differential biogeography-based optimization algorithm and its performance analysis

Biogeography-based optimization (BBO) has drawn a lot of attention as its outstanding performance. However, same with certain typical swarm optimization algorithm, BBO severely suffers from premature convergence problem and the rotational variance of migration operator. In this paper, a two-stage differential biogeography-based optimization (TDBBO) is proposed to address the premature convergence problem and alleviate the rotational variance. In the migration operator, the emigration model is selected according to the two-stage mechanism. The constant emigration model is employed to maintain the diversity of population in the early evolutionary process. The sinusoidal emigration model is selected to accelerate the convergence speed in the late evolutionary process. Meanwhile, the BBO/current-to-select/1, which is a rotationally invariant arithmetic crossover operator, is designed to alleviate the rotational variance. The standard mutation operator is replaced by the Gaussian mutation operator to jump out the local optimum effectively. The greedy selection strategy is introduced to accelerate the convergence speed after the migration and the mutation operators. Besides, the convergence performance of TDBBO is analyzed with the Markov model. Compared with the standard BBO and other outstanding BBO variants on CEC 2017 benchmarks, the TDBBO is superior to the state-of-art BBO variants in terms of solution quality, convergence speed and stability. The TDBBO lays a solid foundation for solving optimization problems of expert and intelligent systems.
Dynamic graph-based label propagation for density peaks clustering

Clustering is a major approach in data mining and machine learning and has been successful in many real-world applications. Density peaks clustering (DPC) is a recently published method that uses an intuitive to cluster data objects efficiently and effectively. However, DPC and most of its improvements suffer from some shortcomings to be addressed. For instance, this method only considers the global structure of data which leading to missing many clusters. The cut-off distance affects the local density values and is calculated in different ways depending on the size of the datasets, which can influence the quality of clustering. Then, the original label assignment can cause a “chain reaction”, whereby if a wrong label is assigned to a data point, and then there may be many more wrong labels subsequently assigned to the other points. In this paper, a density peaks clustering method called DPC-DLP is proposed. The proposed method employs the idea of k-nearest neighbors to compute the global cut-off parameter and the local density of each point. Moreover, the proposed method uses a graph-based label propagation to assign labels to remaining points and form final clusters. The proposed label propagation can effectively assign true labels to those of data instances which located in border and overlapped regions. The proposed method can be applied to some applications. To make the method practical for image clustering, the local structure is used to achieve low-dimensional space. In addition, proposed method considers label space correlation, to be effective in the gene expression problems. Several experiments are performed to evaluate the performance of the proposed method on both synthetic and real-world datasets. The results demonstrate that in most cases, the proposed method outperformed some state-of-the-art methods.
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition

We propose a self-supervised learning method to jointly reason about spatial and temporal context for video recognition. Recent self-supervised approaches have used spatial context [9, 34] as well as temporal coherency [32] but a combination of the two requires extensive preprocessing such as tracking objects through millions of video frames [59] or computing optical flow to determine frame regions with high motion [30]. We propose to combine spatial and temporal context in one self-supervised framework without any heavy preprocessing. We divide multiple video frames into grids of patches and train a network to solve jigsaw puzzles on these patches from multiple frames. So the network is trained to correctly identify the position of a patch within a video frame as well as the position of a patch over time. We also propose a novel permutation strategy that outperforms random permutations while significantly reducing computational and memory constraints. We use our trained network for transfer learning tasks such as video activity recognition and demonstrate the strength of our approach on two benchmark video action recognition datasets without using a single frame from these datasets for unsupervised pretraining of our proposed video jigsaw network.
Automatic data clustering using nature-inspired symbiotic organism search algorithm

The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in an ecosystem. Clustering is a popular data analysis and data mining technique, and k-means clustering is one of the most commonly used methods. However, its effectiveness is highly dependent on the initial solution, and the algorithm may become trapped around local optima. In view of these drawbacks of the k-means method, this paper describes the use of the SOS algorithm to solve clustering problems. Ten standard datasets from the UCI Machine Learning Repository are used to evaluate the effectiveness of SOS against that of optimization algorithms including differential evolution, cuckoo search, flower pollination, particle swarm optimization, artificial bee colony, multi-verse optimizer, and k-means. Experimental results show that the SOS algorithm not only achieves superior accuracy, but also exhibits a higher level of stability.