Reviewerly/exHarmony huggingface dataset:
A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

Robotic exploration of wide terrains, such as agricultural fields, could be challenging while considering the limited robot's capabilities in terms of sensing and power.Thus, in this paper, we proposed OGPR*, an Obstacle Guided Path Refinement algorithm for quickly planning collision-free paths utilizing the obstacles existing in the environment.To tackle the issue of exploring wide terrains, a supervisorybased collaboration between quadcopter and mobile robots is proposed.The quadcopter is responsible for streaming subsequently live two-dimensional images for the environment under discussion while planning safe paths for the ground the mobile robot is planning safe paths to manoeuvre.Numerical simulations proved the significant performance of the proposed OGBR* algorithm when compared to the state of the art algorithms exist in the literature.
An agent-based modeling approach for analyzing the influence of market participants’ strategic behavior on green certificate trading

China has announced that the mandatory green certificate trading would be implemented in 2021. The mandatory green certificate trading not only fosters the liquidity of the green certificate market but also induces the possibility of market participants taking strategic behavior to gain more market returns. Generally, the self-interested strategic behavior was not conducive to improving market efficiency, thereby impeding the implementation of mandatory green certificate trading. To evaluate the influence of market participants’ strategic behavior on the green certificate trading, we embed the strategic behavior model into the artificial green certificate market to simulate the market participants’ strategic behavior in green certificate trading. The results indicate: (1) in green certificate market, the strength of the herd effect determines the average compliance costs level; (2) the value-trading strategy is the optimal strategy for renewable obligation subjects to reduce the compliance costs of mandatory green certificate trading; (3) market participants’ strategic behavior mainly affect the stability of green certificate price, and the greater the price fluctuation, the slower convergence speed to the equilibrium price.
Efficient Optimization of Cut-offs in Quantum Repeater Chains

Quantum communication enables the implementation of tasks that are unachievable with classical resources. However, losses on the communication channel preclude the direct long-distance transmission of quantum information in many relevant scenarios. In principle quantum repeaters allow one to overcome losses. However, realistic hardware parameters make long-distance quantum communication a challenge in practice. For instance, in many protocols an entangled pair is generated that needs to wait in quantum memory until the generation of an additional pair. During this waiting time the first pair decoheres, impacting the quality of the final entanglement produced. At the cost of a lower rate, this effect can be mitigated by imposing a cut-off condition. For instance, a maximum storage time for entanglement after which it is discarded. In this work, we optimize the cut-offs for quantum repeater chains. First, we develop an algorithm for computing the probability distribution of the waiting time and fidelity of entanglement produced by repeater chain protocols which include a cut-off. Then, we use the algorithm to optimize cut-offs in order to maximize secret-key rate between the end nodes of the repeater chain. We find that the use of the optimal cut-off extends the parameter regime for which secret key can be generated and moreover significantly increases the secret-key rate for a large range of parameters.
Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain

Abstract The quality of a movie can be known from the opinions or reviews of previous audiences. This classification of reviews is grouped into positive opinions and negative opinions. One of the data mining algorithms that are most frequently used in research is the Support Vector Machine because it works well as a method of classifying text but has a very sensitive deficiency in the selection of features. The Information Gain method as feature selection can solve problems faster and more stable convergence levels. After testing on two movie review datasets are Cornell and Stanford datasets. The results obtained on the Cornell dataset is the Support Vector Machine algorithm to produce an accuracy of 83.05%, while for the Support Vector Machine based on Information Gain, the accuracy value is 85.65%. Increased accuracy reached 2.6%. Then, the results obtained on the Stanford dataset is the Support Vector Machine algorithm yields a value of 86.46%, while for the Support Vector Machine based on Information Gain, the accuracy value is 86.62%. Increased accuracy reached 0.166%. Support Vector Machine based Information Gain on the problem of movie review sentiment analysis proved to provide more accurate value.
Image Stitching and Rectification for Hand-Held Cameras

In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke. Despite the high complexity of RS geometry, we focus in this paper on a special yet common input—two consecutive frames from a video stream, wherein the inter-frame motion is restricted from being arbitrarily large. This allows us to adopt simpler differential motion model, leading to a straightforward and practical minimal solver. To deal with non-planar scene and camera parallax in stitching, we further propose an RS-aware spatially-varying homogarphy field in the principle of As-Projective-As-Possible (APAP). We show superior performance over state-of-the-art methods both in RS image stitching and rectification, especially for images captured by hand-held shaking cameras.
An End-to-End Framework for Clothing Collocation Based on Semantic Feature Fusion

In this article, we develop an end-to-end clothing collocation learning framework based on a bidirectional long short-term memories (Bi-LTSM) model, and propose new feature extraction and fusion modules. The feature extraction module uses Inception V3 to extract low-level feature information and the segmentation branches of Mask Region Convolutional Neural Network (RCNN) to extract high-level semantic information; whereas the feature fusion module creates a new reference vector for each image to fuse the two types of image feature information. As a result, the feature can involve both low-level image and high-level semantic feature information, so that the performance of Bi-LSTM can be enhanced. Extensive simulations are conducted based on Ployvore and DeepFashion2 datasets. Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.
Seagull

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.
Detecting Alzheimer’s Disease by The Decision Tree Methods Based On Particle Swarm Optimization

Abstract In this study aims to determine the classification of Alzheimer’s disease, this disease is a dangerous disease that can eliminate memory loss and can even result in a loss of ability to remember. For this reason, early detection of this disease is needed so that it can prepare for medical treatment. In this study the proposed method is to compare several decision tree methods with feature or attribute selection using the Particle Swarm Optimization (PSO) algorithm with the Alzheimer OASIS 2 dataset: Longitudinal Data from kaggle.com. The results of experiments with ten-fold cross validation, by testing the decision tree algorithm before the feature or attribute selection is performed, the highest accuracy value is obtained from the random forest algorithm with a value of 91.15%. The feature selection process is carried out using the PSO algorithm and the experiment is repeated using the Decision tree, the PSO-based random forest algorithm has the highest accuracy value of 93.56% with a kappa value of 0.884. Feature or attribute selection using the PSO algorithm is proven to be able to improve the accuracy of the decision tree algorithm, and is included in the algorithm with a very good range of values.
Weakly Supervised Learning for Over-Segmentation Based Handwritten Chinese Text Recognition

In this paper, we proposed a weakly supervised learning method for string-level training of character classifier in over-segmentation based handwritten Chinese text recognition (HCTR). The over-segmentation based framework can easily integrate multiple context models and provide accurate character boundary and recognition confidence, but has not been implemented with string-level training for HCTR. We propose to optimize the character classifier by minimizing the marginal log-likelihood on a string-level annotated handwriting dataset, where the forward-backward algorithm is utilized in a segmentation-and-recognition lattice. Experimental results on the CASIA-HWDB and ICDAR-2013 competition datasets show that the proposed method improves the recognition performance significantly, which demonstrates its effectiveness.
Financial development, financial structure and economic growth in the Sub‐Saharan African countries

Abstract We use a financial development indicator to revisit the relationship of financial development and financial structure with economic growth. The financial development indicator simultaneously takes into account accessibility, depth and financial efficiency for the period 1980–2017 for Sub‐Saharan Africa countries. The continuously updated fully modified (Cup‐FM) and continuously updated bias corrected (Cup‐BC) estimators, as well as the panel smooth transition regression (PSTR), were used to examine the linear and nonlinear effects of financial development and financial structure on economic growth. For low‐income countries, the Cup‐FM and the Cup‐BC estimators indicate that the financial system relies on financial markets. The findings obtains through the PSTR model confirm this conclusion when the financial market represents more than 34% of financial institutions. For middle‐income countries, the Cup‐FM and Cup‐BC estimators reveal that the financial system relies on banking industry. However, this result is not supported by the PSTR model. In fact, it indicates that the financial system in these countries is based on financial markets when government expenditure as percentage of gross domestic product is less than 2.62%. Above this threshold, the financial system depends on the development of financial services, legal and regulatory framework.
Harnessing Policy Authenticity for Hidden Ciphertext Policy Attribute-Based Encryption

The field of cryptography has endeavored to solve numerous security problems. However, a common premise of many of those problems is that the encryptor always generates the ciphertext correctly. Around 10 years ago, this premise was not a problem. However, due to the rapid development and the use of the cloud, which has introduced various access policies and functionalities to provide higher security, it is not correct to assume that this premise is always applied. A “Fake Policy Attack”, which we introduce in this article, is an attack that incorrectly sets the access policy of the ciphertext against the system rules so that users who do not meet the rules can decrypt the ciphertext. In other words, it is an attack that ignores the rules of the system and eventually breaks the security and leaks information. This attack can be more critical for the application environments that require strong security not to leak any related information about ciphertext. In this article, we demonstrate the possible threat of the Fake Policy Attack by providing two relevant examples. Then, we propose a scheme called Policy Authenticable ABE (PA-ABE) to resolve this issue. We provide a formal security analysis of the proposed scheme and performance evaluation results based on our implementation.
Pixel‐wise Dense Detector for Image Inpainting

Abstract Recent GAN‐based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts. Moreover, the adversarial loss and reconstruction loss (e.g., ℓ 1 loss) are combined with tradeoff weights, which are also difficult to tune. In this paper, we propose a novel detection‐based generative framework for image inpainting, which adopts the min‐max strategy in an adversarial process. The generator follows an encoder‐decoder architecture to fill the missing regions, and the detector using weakly supervised learning localizes the position of artifacts in a pixel‐wise manner. Such position information makes the generator pay attention to artifacts and further enhance them. More importantly, we explicitly insert the output of the detector into the reconstruction loss with a weighting criterion, which balances the weight of the adversarial loss and reconstruction loss automatically rather than manual operation. Experiments on multiple public datasets show the superior performance of the proposed framework. The source code is available at https://github.com/Evergrow/GDN_Inpainting.
Power forecasting of three silicon-based PV technologies using actual field measurements

This work focuses on short-term photovoltaic (PV) power forecasting each 5 min of the following day of a 5.94 kWp grid-connected PV plant located in Safi, Morocco. The PV system is composed of three silicon technologies: mono-crystalline (m-Si), poly-crystalline (p-Si) and amorphous (a-Si). The field measurements from June 18, 2016 to July 15, 2018 were used to build both linear and nonlinear models. These suggested models have been compared with the ones presented in the literature, including the persistence and an Artificial Neural Network (ANN) models widely used for short-term PV output forecasting. The comparison has been performed on statistical scores: MBE, MAE, MAPE, RMSE, and nRMSE. The two proposed models maintain simple mathematical expressions and involve only a small number of predictors. The latter are plane of array solar irradiance and module or ambient temperatures. The models are also able to follow the time-varying of solar irradiance. The findings showed that the suggested models outperform all the tested ones and achieved better performances in terms of prediction accuracy. In fact, MAE and RMSE of our models do not exceed 2.107% and 2.674% respectively, whereas they may achieve respectively 2.488% and 5.796% for persistence and 4.430% and 5.650% for ANN.
Attention-based learning of self-media data for marketing intention detection

In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.68% in contrast with ATT-CNN. It illustrates that our A-CNN can effectively address semantic and feature selection for marketing intention detection.
A Novel Two-Step Strategy Based on White-Balancing and Fusion for Underwater Image Enhancement

Images captured from underwater environment always suffer from color distortion, detail loss, and contrast reduction due to the medium scattering and absorption. This paper introduces an enhancement approach to improve the visual quality of underwater images, which does not require any dedicated devices or additional information more than the native single image. The proposed strategy consists of two steps: an improved white-balancing approach and an artificial multiple underexposure image fusion strategy for underwater imaging. In our white-balancing approach, the optimal color-compensated approach is determined by the sum of the Underwater Color Image Quality Evaluation (UCIQE) and the Underwater Image Quality Measure (UIQM). We get an optimal white-balanced version of the input by combining the well-known Gray World assumption and the optimal channel-compensated approach. In our artificial multiple underexposure image fusion strategy, first the gamma-correction operation is adopted to generate multiple underexposure versions. Then we propose to use `contrast', `saturation', and `well-exposedness' as three weights, to be blended into the well-known multi-scale fusing scheme. Images enhanced by our strategy have a better visual quality than some state-of-the-art underwater dehazing techniques, through our validation with a wide range of qualitative and quantitative evaluations.
Breast Cancer Prediction Using Machine Learning

Breast cancer may be a prevalent explanation for death, and it's the sole sort of cancer that's widespread among women worldwide. The prime objective of this paper creates the model for predicting breast cancer using various machine learning classification algorithms like k Nearest Neighbor (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Gaussian Naive Bayes (NB). And furthermore, assess and compare the performance of the varied classifiers as far as accuracy, precision, recall, f1-Score, and Jaccard index. The breast cancer dataset is publicly available on the UCI Machine Learning Repository and therefore the implementation phase dataset is going to be partitioned as 80% for the training phase and 20% for the testing phase then apply the machine learning algorithms. k Nearest Neighbors achieved a significant performance in respect of all parameters.
A Two-Stage Algorithm for the Detection and Removal of Random-Valued Impulse Noise Based on Local Similarity

A two-stage denoising algorithm based on local similarity is proposed to process lowly and moderate corrupted images with random-valued impulse noise in this paper.In the noise detection stage, the pixel to be detected is centered and the local similarity between the pixel and each pixel in its neighborhood is calculated, which can be used as the probability that the pixel is noise.By obtaining the local similarity of each pixel in the image and setting an appropriate threshold, the noise pixels and clean pixels in the damaged image can be detected.In the image restoration stage, an improved bilateral filter based on local similarity and geometric distance is designed.The pixel detected as noise in the first stage is filtered and the new intensity value is the weighted average of all pixel intensities in its neighborhood.A large number of experiments have been conducted on different test images and the results show that compared with the mainstream denoising algorithms, the proposed method can detect and filter out the random-value impulse noise in the image more effectively and faster, while better retaining the edges and other details of the image.
Interplay between financial sector and institutional framework in the economic growth process of Kenya

In line with the recent assertion that institutional quality matters in the way financial development influences economic growth in financial economics literature, this study examines the moderating role of institutional quality in the finance‐growth nexus in Kenya for the period 1986–2015. A fully modified ordinary least square (FMOLS) estimator is adopted to take care of endogeneity problem inherent in the relationship among the variables while error‐correction version of autoregressive distributed lag (ARDL) technique is adopted to take care of short‐ and long‐run effects. The research outputs reveal that in the short‐run institutions and finance complement and reinforce each other to enhance growth, while institutional framework in Kenya constitutes a drain that leaks out the growth benefits of financial development in the long run. It is also found that the country majorly operates below the threshold level of institutional quality required to maximize the growth benefits of financial development. This study, therefore, concludes that institutional framework guiding the operations of financial system in Kenya should be strengthened so as to block loopholes and lapses that leak out the growth benefits of finance. JEL CLASSIFICATION D53; E02; N27; O43
Human-Machine Cooperative Video Anomaly Detection

It is still a challenge to detect anomalous events in video sequences in the field of computer vision due to heavy object occlusions, varying crowded densities and complex situations. To address this, we propose a novel human-machine cooperative approach which uses human feedback on anomaly confirmation to inform and enhance video anomaly detection. Specifically, we analyze the spatio-temporal characteristics of sequential frames of a video from the appearance and motion perspective from which spatial and temporal features are identified and extracted. We then develop a convolutional autoencoder neural network to compute an abnormal score based on reconstruction errors. In this process, a group of experts will provide human feedback to a certain proportion of classified frames to be incorporated into the model, and also the final judgment for the event anomalies for training and classification. The proposed approach is evaluated on 3 publicly available surveillance datasets, showing improved accuracy and competitive performance (93.7% AUC) with respect to the best performance (90.6% AUC) of the state-of-the-art approaches. The approach has not been previously seen to the best of our knowledge.