Reviewerly/exHarmony huggingface dataset:
News Recommendation with Topic-Enriched Knowledge Graphs

News recommendation systems? purpose is to tackle the immense amount of news and offer personalized recommendations to users. A major issue in news recommendation is to capture the precise news representations for the efficacy of recommended items. Commonly, news contents are filled with well-known entities of different types. However, existing recommendation systems overlook exploiting external knowledge about entities and topical relatedness among the news. To cope with the above problem, in this paper, we propose Topic-Enriched Knowledge Graph Recommendation System(TEKGR). Three encoders in TEKGR handle news titles in two perspectives to obtain news representation embedding: (1) to extract meaning of news words without considering latent knowledge features in the news and (2) to extract semantic knowledge of news through topic information and contextual information from a knowledge graph. After obtaining news representation vectors, an attention network compares clicked news to the candidate news in order to get the user's final embedding. Our TEKGR model is superior to existing news recommendation methods by manipulating topical relations among entities and contextual features of entities. Experimental results on two public datasets show that our approach outperforms state-of-the-art deep recommendation approaches.
Duplicate Bug Report Detection and Classification System Based on Deep Learning Technique

Duplicate bug report detection is a process of finding a duplicate bug report in the bug tracking system. This process is essential to avoid unnecessary work and rediscovery. In typical bug tracking systems, more than thousands of duplicate bug reports are reported every day. In turn, human cost, effort and time are increased. This makes it an important problem in the software management process. The solution is to automate the duplicate bug report detection system for reducing the manual effort, thus the productivity of triager's and developer's is increased. It also speeds up the process of software management as a result software maintenance cost is also reduced. However, existing systems are not quite accurate yet, in spite of these systems used various machine learning approaches. In this work, an automatic bug report detection and classification model is proposed using deep learning technique. The proposed system has three modules i.e. Preprocessing, Deep Learning Model and Duplicate Bug report Detection and Classification. Further, the proposed model used Convolutional Neural Network based deep learning model to extract relevant feature. These relevant features are used to determine the similar features of bug reports. Hence, the bug reports similarity is computers through these similar features. The performance of the proposed system is evaluated on six publicly available datasets using six performance metrics. It is noticed that the proposed system outperforms the existing systems by achieving an accuracy rate in the range of 85% to 99 % and recall@k rate in between 79%-94%. Moreover, the effectiveness of the proposed system is also measured on the cross training datasets of same and different domain. The proposed system achieves a good high accuracy rate for same domain data sets and low accuracy rate for different domain datasets.
Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods

Hyperspectral (HS) imaging holds great potential for the mapping of geological targets. Innovative acquisition modes such as drone-borne or terrestrial remote sensing open up new scales and angles of observation, which allow to analyze small-scale, vertical, or difficult-to-access outcrops. A variety of available sensors operating in different spectral ranges can provide information about the abundance and spatial location of various geologic materials. However geological outcrops are inherently uneven and spectrally heterogeneous, may be covered by dust, lichen or weathering crusts, or contain spectrally indistinct objects, which is why classifications or domain mapping approaches are often used in geoscientific and mineral exploration applications as a means to discriminate mineral associations (e.g. ore or alteration zones) based on overall variations in HS data. Feature extraction (FE) algorithms are prominently used as a preparatory step to identify the first order variations within the data and, simultaneously, reduce noise and data dimensionality. The most established FE algorithms in geosciences are, by far, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF). Major progress has been conducted in the image processing community within the last decades, yielding innovative FE methods that incorporate spatial information for smoother and more accurate classification results. In this paper, we test the applicability of conventional (PCA, MNF) and innovative FE techniques (OTVCA: Orthogonal total variation component analysis and WSRRR: Wavelet-based sparse reduced-rank regression) on three case studies from geological HS mapping campaigns, including drone-borne mineral exploration, terrestrial paleoseismic outcrop scanning and thermal HS lithological mapping. This allows us to explore the performance of different FE approaches on complex geological data with sparse or partly inaccurate validation data. For all case studies, we demonstrate advantages of innovative FE algorithms in terms of classification accuracy and geological interpretability. We promote the use of advanced image processing methods for applications in geoscience and mineral exploration as a tool to support geological mapping activities.
An Index Advisor Using Deep Reinforcement Learning

We study the problem of index selection to maximize the workload performance, which is critical to database systems. In contrast to existing methods, we seamlessly integrate index recommendation rules and deep reinforcement learning, such that we can recommend single-attribute and multi-attribute indexes together for complex queries and meanwhile support multiple-index access to a table. Specifically, we first propose five heuristic rules to generate the index candidates. Then, we formulate the index selection problem as a reinforcement learning task and employ Deep Q Network (DQN) on it. Using the heuristic rules can significantly reduce the dimensions of the action space and state space in reinforcement learning. With the neural network used in DQN, we can model the interactions between indexes better than previous methods. We conduct experiments on various workloads to show its superiority.
Performance Modeling of Serverless Computing Platforms

Analytical performance models have been leveraged extensively to analyze and improve the performance and cost of various cloud computing services. However, in the case of serverless computing, which is projected to be the dominant form of cloud computing in the future, we have not seen analytical performance models to help with the analysis and optimization of such platforms. In this work, we propose an analytical performance model that captures the unique details of serverless computing platforms. The model can be leveraged to improve the quality of service and resource utilization and reduce the operational cost of serverless platforms. Also, the proposed performance model provides a framework that enables serverless platforms to become <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workload-aware</i> and operate differently for different workloads to provide a better trade-off between the cost and performance depending on the user's preferences. The current serverless offerings require the user to have extensive knowledge of the internals of the platform to perform efficient deployments. Using the proposed analytical model, the provider can simplify the deployment process by calculating the performance metrics for users even before physical deployments. We validate the applicability and accuracy of the proposed model by extensive experimentation on AWS Lambda. We show that the proposed model can calculate essential performance metrics such as average response time, probability of cold start, and the average number of function instances in the steady-state. Also, we show how the performance model can be used to tune the serverless platform for each workload, which will result in better performance or lower cost without scarifying the other. The presented model assumes no non-realistic restrictions, so that it offers a high degree of fidelity while maintaining tractability at large scale.
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms

Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriate evaluation of recommendation algorithms. In this paper, we revisit alternative experimental settings for evaluating top-N recommendation algorithms, considering three important factors, namely dataset splitting, sampled metrics and domain selection. We select eight representative recommendation algorithms (covering both traditional and neural methods) and construct extensive experiments on a very large dataset. By carefully revisiting different options, we make several important findings on the three factors, which directly provide useful suggestions on how to appropriately set up the experiments for top-N item recommendation.
Gradient and Newton boosting for classification and regression

Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updates is often not made in both applied and methodological research, and it is thus implicitly assumed that the difference is irrelevant. The goal of this article is to clarify this situation. In particular, we present gradient and Newton boosting, as well as a hybrid variant of the two, in a unified framework. We compare these boosting algorithms with trees as base learners using various datasets and loss functions. Our experiments show that Newton boosting outperforms gradient and hybrid gradient-Newton boosting in terms of predictive accuracy on the majority of datasets. We also present evidence that the reason for this is not faster convergence of Newton boosting. In addition, we introduce a novel tuning parameter for tree-based Newton boosting which is interpretable and important for predictive accuracy.
Smooth pinball loss nonparallel support vector machine for robust classification

In this paper, we propose a robust smooth pinball loss nonparallel support vector machine (SpinNSVM) for binary classification. We first define a smooth pinball loss function, which is insensitive to the feature noise of samples, especially for the samples located nearby the boundary, and is able to capture the distribution of samples well simultaneously. Due to its differentiability, SpinNSVM model is formulated with two convex optimization problems. Furthermore, an efficient dual coordinate descent algorithm is adopted to solve the dual formulations of SpinNSVM. Numerical experiments are provided to demonstrate the effectiveness and the efficiency of the proposed method.
CPU Based YOLO: A Real Time Object Detection Algorithm

This paper describes CPU Based YOLO, a real time object detection model to run on Non-GPU computers that may facilitate the users of low configuration computer. There are a lot of well improved algorithms for object detection such as YOLO, Faster R-CNN, Fast R-CNN, R-CNN, Mask R-CNN, R-FCN, SSD, RetinaNet etc. YOLO is a Deep Neural Network algorithm for object detection which is most fast and accurate than most other algorithms. YOLO is designed for GPU based computers which should have above 12GB Graphics Card. In our model, we optimize YOLO with OpenCV such a way that real time object detection can be possible on CPU based Computers. Our model detects object from video in 10.12 - 16.29 FPS and with 80-99% confidence on several Non -GPU computers. CPU Based YOLO achieves 31.05% mAP.
Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot

In this article, we propose a new scheme for a fuzzy logic controller, which includes acceleration as one of its linguistic variables, as opposed to other techniques and approaches that have been developed and reported in the literature. This method is used for controlling the tracking of the trajectory followed by the joints of a 2-DoF manipulator robot. To this end, a complete simulation environment is developed through the MatLab/Simulink® software. The dynamic model of the manipulator robot includes a vector that consists of the estimate of the friction forces present in the joints. Then, a controller based on fuzzy logic is designed and implemented for each joint. Finally, the performance of the developed system is assessed and then compared to the performance of a classic PID controller. The incorporation of the fuzzy variable acceleration significantly improved the system’s response.
Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

Segmentation is an essential preprocessing step in techniques for image analysis. The automatic segmentation of brain magnetic resonance imaging has been exhaustively investigated since the accurate use of this kind of methods permits the diagnosis and identification of several diseases. Thresholding is a straightforward and efficient technique for image segmentation. Nonetheless, thresholding based approaches tend to increase the computational cost based on the number of thresholds used for the segmentation. Therefore, metaheuristic algorithms are an important tool that helps to find the optimal values in multilevel thresholding. The adaptive differential evolution, based in numerous successes through history, with linear population size reduction (LSHADE) is a robust metaheuristic algorithm that efficiently solves numerical optimization problems. The main advantage of LSHADE is its capability to adapt its internal parameters according to prior knowledge acquired along the evolutionary process. Meanwhile, the continuous reduction of the population improves the exploitation process. This article presents a multilevel thresholding approach based on the LSHADE method for the segmentation of magnetic resonance brain imaging. The proposed method has been tested using three groups of reference images— the first group consists of grayscale standard benchmark images, the second group consists of magnetic resonance T2-weighted brain images, and the third group is formed by images of unhealthy brains affected by tumors. In turn, the performance of the intended approach was compared with distinct metaheuristic algorithms and machine learning methods. The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality.
RFID-Pose: Vision-Aided Three-Dimensional Human Pose Estimation With Radio-Frequency Identification

In recent years, human pose tracking has become an important topic in computer vision (CV). To improve the privacy of human pose tracking, there is considerable interest in techniques without using a video camera. To this end, radio-frequency identification (RFID) tags, as a low-cost wearable sensor, provide an effective solution for 3-D human pose tracking. In this article, we propose RFID-Pose, a vision-aided realtime 3-D human pose estimation system, which is based on deep learning assisted by CV. The RFID phase data are calibrated to effectively mitigate the severe phase distortion, and high accuracy low rank tensor completion is employed to impute the missing RFID data. The system then estimates the spatial rotation angle of each human limb, and utilizes the rotation angles to reconstruct human pose in realtime with the forward kinematic technique. A prototype is developed with commodity RFID devices. High pose estimation accuracy and realtime operation of RFID-Pose are demonstrated in our experiments using Kinect 2.0 as a benchmark.
A Hybrid Framework for Underwater Image Enhancement

Underwater captured images often suffer from poor visibility caused by two major degradations: scattering and absorption. In this paper, we propose a hybrid framework for underwater image enhancement, which unifies underwater white balance and variational contrast and saturation enhancement. In our framework, the improved underwater white balance (UWB) algorithm is integrated with histogram stretching, aiming to better compensate the attenuation difference along the propagation path and remove undesired color castings. In addition, a variational contrast and saturation enhancement (VCSE) model is developed based on the enhanced result obtained from UWB. The advantages of VCSE model lie in the improvements of contrast and saturation as well as the elimination of hazy appearance induced by scattering. Moreover, we design a fast Gaussian pyramid-based algorithm to speed up the solving of VCSE model. The improvements achieved by our method include the more effective in color correction, haze removal and detail clarification. Extensive qualitative and quantitative assessments demonstrate that the proposed approach obtains high quality outcomes, which outperforms several state-of-the-art methods. Application tests further verify the effectiveness and broad application prospects of our proposed method.
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition

This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel Skeleton Points Interaction Learning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets.
Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

The power start-up operation of a nuclear power plant (NPP) increases the reactor power to the full-power condition for electricity generation. Compared to full-power operation, the power-increase operation requires significantly more decision-making and therefore increases the potential for human errors. While previous studies have investigated the use of artificial intelligence (AI) techniques for NPP control, none of them have addressed making the relatively complicated power-increase operation fully autonomous. This study focused on developing an algorithm for converting all the currently manual activities in the NPP power-increase process to autonomous operations. An asynchronous advantage actor-critic, which is a type of deep reinforcement learning method, and a long short-term memory network were applied to the operator tasks for which establishing clear rules or logic was challenging, while a rule-based system was developed for those actions, which could be described by simple logic (such as if-then logic). The proposed autonomous power-increase control algorithm was trained and validated using a compact nuclear simulator (CNS). The simulation results were used to evaluate the algorithm's ability to control the parameters within allowable limits, and the proposed power-increase control algorithm was proven capable of identifying an acceptable operation path for increasing the reactor power from 2% to 100% at a specified rate of power increase. In addition, the pattern of operation that resulted from the autonomous control simulation was found to be identical to that of the established operation strategy. These results demonstrate the potential feasibility of fully autonomous control of the NPP power-increase operation.
Color difference classification of dyed fabrics via a kernel extreme learning machine based on an improved grasshopper optimization algorithm

Abstract Conventional artificial color difference detection is error prone and inefficient. Herein, a novel color difference classification model is proposed for dyed fabrics via a kernel extreme learning machine based on an improved grasshopper optimization algorithm. First, in order to prevent the grasshopper optimization algorithm from succumbing to local optimality, it is proposed to optimize the initial population of the algorithm using differential evolution, resulting in a better solution direction at the outset. Then, this novel grasshopper algorithm is used to adjust the kernel bandwidth and penalty parameters of the learning maching model, thereby forming a color difference classification model for dyed fabrics based on the differential evolution grasshopper optimization algorithm kernel extreme learning machine. Finally, the key indicator values representing color difference are extracted from the printed and dyed product images collected under the standard light source. The color difference calculated by substituting these values into the color difference formula generates a color difference dataset, which is used to train and test the color difference classification model. Experimental results show that the proposed differential evolution grasshopper optimization algorithm kernel extreme learning machine model has high classification accuracy and impressive stability. The average classification accuracy of the proposed model is 98.89%, whereas the accuracy of kernel extreme learning machine is only 91.08%.
A Quadrotor With an Origami-Inspired Protective Mechanism

Despite advances in localization and navigation, aerial robots inevitably remain susceptible to accidents and collisions. In this letter, we propose a passive foldable airframe as a protective mechanism for a small aerial robot. A foldable quadrotor is designed and fabricated using the origami-inspired manufacturing paradigm. Upon an accidental mid-flight collision, the deformable airframe is mechanically activated. The rigid frame reconfigures its structure to protect the central part of the robot that houses sensitive components from a crash to the ground. The proposed robot is fabricated, modeled, and characterized. The 51-g vehicle demonstrates the desired folding sequence in less than 0.15 s when colliding with a wall when flying.