Reviewerly/exHarmony huggingface dataset:
Customized reputation generation of entities using sentiment analysis

Purpose In this study, the authors have used the customer reviews of books and movies in natural language for the purpose of sentiment analysis and reputation generation on the reviews. Most of the existing work has performed sentiment analysis and reputation generation on the reviews by using single classification models and considered other attributes for reputation generation. Design/methodology/approach The authors have taken review, helpfulness and rating into consideration. In this paper, the authors have performed sentiment analysis for extracting the probability of the review belonging to a class, which is further used for generating the sentiment score and reputation of the review. The authors have used pre-trained BERT fine-tuned for sentiment analysis on movie and book reviews separately. Findings In this study, the authors have also combined the three models (BERT, Naïve Bayes and SVM) for more accurate sentiment classification and reputation generation, which has outperformed the best BERT model in this study. They have achieved the best accuracy of 91.2% for the movie review data set and 89.4% for the book review data set which is better than the existing state-of-art methods. They have used the transfer learning concept in deep learning where you take knowledge gained from one problem and apply it to a similar problem. Originality/value The authors have proposed a novel model based on combination of three classification models, which has outperformed the existing state-of-art methods. To the best of the authors’ knowledge, there is no existing model which combines three models for sentiment score calculation and reputation generation for the book review data set.
Application of text mining employing k-means algorithms for clustering tweets of Tokopedia

Abstract In this current digital era, people tend to shop online. Because of that, there are currently many e-commerce companies that can satisfy the various needs of society in shopping. Each company certainly has a strategy to attract consumers to shop at their shopping place. One of the media commonly used to attract consumers is social media. Tokopedia is one of the biggest marketplaces in Indonesia and is also active in utilizing Twitter as their social media mean. Therefore, it is essential for Tokopedia to pay attention to tweet contents interesting enough to be publicized. By applying text mining using K-Means Clustering algorithm, it can be seen which types of tweet contents that are attractive for Tokopedia consumers. Out of 885 Tokopedia tweets that have been collected, a clustering is then done using K-means algorithm, resulting 48 cluster tweets . Then, from the 48 clusters, they are further grouped into 5 major groups. Based on the results of the grouping, it can be seen that the most interesting content deals with quiz prizes and the least attractive content is on lifestyle.
Disentangled Contour Learning for Quadrilateral Text Detection

Precise detection of quadrilateral text is of great significance for subsequent recognition, where the main challenge comes from four distorted sides. Existing methods concentrate on learning four vertices to construct the contour. However, vertices are dummy intersections entangled by their neighbor sides. The regression of each vertex would simultaneously affect its two neighbor sides. As a result, the originally independent side would be influenced by two different vertices which further inevitably disturb other sides. The above entangled vertices learning suppresses the learning efficiency and detection performance. In this paper, we proposed disentangled contour learning network (DCLNet) to focus on clear regression of each individual side disentangled from the whole quadrilateral contour. The side is parameterized by a linear equation that disentangled in the polar coordinates for easier learning. With tailored Ray-IoU loss and sine angle loss, DCLNet could better learn the representation of each disentangled side without being disturbed by others. The final quadrilateral text contour is easily constructed by intersecting the predicted linear equations of sides. Empirically, the proposed DCLNet achieves state-of-the-art detection performances on three scene text benchmarks. Ablation study is also presented to demonstrate the effectiveness of proposed disentangled contour learning framework.
From Virtual Worlds to Mirror Worlds: A Model and Platform for Building Agent-Based eXtended Realities

Extended Reality (XR) refers to applications that blend the digital and the physical worlds in different ways: both by situating virtual worlds into physical environments by means of Augmented and Mixed Reality Technologies, and by exploiting smart things and devices in the physical environment connected to the Virtual World, in a pervasive computing perspective. Like in the case of Virtual Worlds and Intelligent Virtual Environments, XR applications are a relevant application domain for multi-agent systems and AI—for instance, for designing XR-based smart environments. The research question addressed by this paper is about the definition of a model for conceiving and designing agent-based XR applications, effective enough to capture essential aspects in spite of the specific implementing technologies. To this purpose, the paper describes a model based on the Mirror World conceptual framework and a concrete platform used to evaluate its strengths and weaknesses.
Video stabilization algorithm based on virtual sphere model

We propose a video stabilization algorithm based on the rotation of a virtual sphere. Unlike traditional video stabilization algorithms relying on two-dimensional motion models or reconstruction of (3D) camera motions, the proposed virtual sphere model stabilizes video by projecting each frame onto the sphere and performing corrective rotations. Specifically, matching feature points between two adjacent frames are first projected onto two virtual spheres to obtain pairs of spherical points. Then, the rotation matrix between the previous and current frame is calculated. The resulting 3D rotation matrix sequence is used to represent the camera motion, and it is smoothed using the geodesic distance on a Riemannian manifold. Finally, the difference between the smoothed and original path allows obtaining the rotation matrix that causes camera jitter, and the virtual spheres are rotated reversely to suppress jitter. Experimental results show that the proposed algorithm can effectively reduce random jitter, outperforming state-of-the-art methods.
DeepMark++: Real-time Clothing Detection at the Edge

Clothing recognition is the most fundamental AI application challenge within the fashion domain. While existing solutions offer decent recognition accuracy, they are generally slow and require significant computational resources. In this paper we propose a single-stage approach to overcome this obstacle and deliver rapid clothing detection and keypoint estimation. Our solution is based on a multi-target network CenterNet [26], and we introduce several powerful post-processing techniques to enhance performance. Our most accurate model achieves results comparable to state-of-the-art solutions on the DeepFashion2 dataset [4], and our light and fast model runs at 17 FPS on the Huawei P40 Pro smartphone. In addition, we achieved second place in the DeepFashion2 Landmark Estimation Challenge 2020 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with 0.582 mAP on the test dataset.
Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks

This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right.
Adaptive State Estimation and Real-Time tracking of Aeroelastic Wings with Augmented Kalman filter and Kernelized Correlation Filter

Advancements in aircraft controllers and the tendency towards increasingly lighter and more flexible aircraft designs create the need for adaptive and intelligent control systems.While lighter aircraft structures have the potential to show better structural and aerodynamic efficiency, they are also more susceptible to dynamic loads.A key aspect to account for the flexibility of the structure in a closed-loop design is aeroelastic state estimation and the feedback of wing motion (elastic states).A potential non-invasive approach to provide this measurement for control-feedback is visual tracking, with fuselage-mounted cameras observing the motion of the wing.In particular, high-speed visual tracking with correlation filters such as KCF (Kernelized Correlation Filter), allow to efficiently and robustly correlate between two samples with kernelized linear regression.A purely visual tracking filter however does not contain information regarding the dynamics of the system subject to tracking and may fail under marker loss and occlusion.To increase the robustness of the racking an EKF (extended Kalman filter) is added to the tracking filter acting as a KCF-EKF tracking couple.The Kalman filter is further augmented into augmented Kalman filter form, to allow joint on-line estimation of the model states and parameters.This proposed tracking approach is used to adaptively reconstruct the motion of a very flexible wing in real-time subject to gust excitation in the OJF (Open Jet Facility) wind tunnel at the Technical University of Delft.The method shows a good agreement with time and frequency domain analysis of the reference data measured by a laser vibrometer and demonstrated the effectiveness of KCF-AEKF couple under the presence of marker loss and model uncertainties for a model-free control approach.
A capsule-based collision detection approach of irregular objects in virtual maintenance

Purpose Virtual maintenance simulation is of great importance to help designers find and avoid design problems. During its simulation phase, besides the high precision requirement, collision detection must be suitable for all irregular objects in a virtual maintenance environment. Therefore, in this paper, a collision detection approach is proposed based on encapsulation for irregular objects in the virtual maintenance environment. Design/methodology/approach First, virtual maintenance simulation characteristics and several commonly used bounding boxes methods are analyzed, which motivates the application of encapsulation theory. Based on these, three different encapsulation methods are oriented to the needs of simulation, including encapsulation of rigid maintenance objects, flexible maintenance objects and maintenance personnel. In addition, to detecting collisions accurately, this paper divides the detection process into two stages. That is, in the first stage, a rough detection is carried out and then a tiny slice space is constructed to generate corresponding capsule groups, which will be redetected in the secondary stage. At last, several case studies are applied to illustrate the performance of the methodology. Findings The automatic construction algorithm for bounding boxes can be adapted to all forms of objects. The number of detection primitives are greatly reduced. It introduces the reachable space of the human body in maintainability as the collision search area. Originality/value The advantages of virtual maintenance simulation could also be advantageous in the industry with further studies. The paper believes this study is of particular interest to the readers of your journal.
Multi-Agent Reinforcement Learning using the Deep Distributed Distributional Deterministic Policy Gradients Algorithm

In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent action in a cooperative game environment. The algorithm is experimented on training the agents to play a game of tennis against each other. The architectures of the actor and cretic networks are meticulously designed and the D4PG hyperparameters are carefully tuned. The trained agents are successfully tested in the Unity Machine Learning Agents environment. The testing shows the powerful performance of the D4PG algorithm in training multi-agents in complex environments.
Improvement of U-Net architecture for image binarization with activation functions replacement

In this work we study the effect of activation functions in a neural network. We consider how activation functions with different properties and their combination affect the final quality of the model. Due to optimization and speed performance issues with most of bounded functions that are represented by sigmoids, we propose the generalized version of SoftSign function - ratio function (rf). Its shape greatly depends on introduced degree parameter, which in theory leads to new interesting property - contraction to zero. For evaluation, we chose image binarization problem: based on UNet architecture of DIBCO-2017 winners, we conducted all experiments with replacing activation functions only. Our research has led us to the state-of-the-art results in binarization quality on DIBCO-2017 test dataset. U-Net with modified activation functions significantly outperforms all existing solutions in all metrics.
Improvement of similarity–diversity trade-off in recommender systems based on a facility location model

Abstract There is a growing interest in the offering of novel alternative choices to users of recommender systems. These recommendations should match the target query while at the same time they should be diverse with each other in order to provide useful alternatives to the user, i.e., novel recommendations. In this paper, the problem of extracting novel recommendations, under the similarity–diversity trade-off, is modeled as a facility location problem. The results from tests in the benchmark Travel Case Base were satisfactory when compared to well-known recommender techniques, in terms of both similarity and diversity. It is shown that the proposed method is flexible enough, since a parameter of the adopted facility location model constitutes a regulator for the trade-off between similarity and diversity. Also, our work can broaden the perspectives of the interaction and combination of different scientific fields in order to achieve the best possible results.
Design and Optimization of an Encased Drone for Underground Mining Applications

To date, the drone technology have been used for many applications in the mining industry. Currently, on surface, the drone technology is being used for surveying, geotechnical monitoring, and safety inspection, to name a few. However, the underground mines still cannot use the drone properly due to many challenges like confined space, GPS-denied environment, various air density, dynamic environment, and equipment. To address these challenges, this paper proposes an encased multi-rotor drone that is protected in a cage and can hover in monitoring hard to reach areas which makes it more controllable. In the first step, a multirotor drone is designed with the capabilities to carry a cage. The cage is designed by a combination of pentagon and triangle holes for extending the sensor vision. The designed connection joints of the cage simulated by the finite element method and the result have been used to improve connection joints and cage stability. The design of the connection ring of the cage and drone is tested in the lab and improved after the drop test results. Finally, the flight simulation of the drone is done for various air density and payload. The results show that the drone performance increase by increasing the air density and the drone can fly in different air density of underground mine. Also, the result shows that the drone can carry up to 450 grams payload or an extra sensor for monitoring underground spaces.
Modern Challenges for Oil and Gas Education

Today, in education in general and in higher education in particular, there is no more pressing problem than the organization of the educational process in the context of a pandemic that all universities in the world are facing. Also, there are no universal recipes for how to organize educational and scientific processes in a university without losing quality. The authors share their experience on how to restructure the work of teachers with students in the context of distance communication, organize the work of an educational portal, conduct training sessions, internships, state final certification, create motivational remuneration for the teaching staff. The article concludes that the challenges of the pandemic to higher education made it possible to find algorithms for interaction between students and teachers in the online environment, showed the need to improve the methodological qualifications of teachers, motivational and psychological support for participants in the educational process, accelerate the development of digital resources.
A stable feature selection method based on relevancy and redundancy

Abstract In this paper, the characteristics of software defect prediction are analyzed from the perspective of machine learning. To solve the problem of some redundant or uncorrelated features in defect data sets, a stable feature selection method based on relevancy and redundancy (RRSFS) is proposed. RRSFS combines the redundancy between features and the correlation between features and classes to select the optimal subset. RRSFS not only reduces the cost of data operation in the prediction model, but also enhances the stability of feature selection algorithm.
The Read-Only Semi-External Model

Previous chapter Next chapter Full AccessProceedings Symposium on Algorithmic Principles of Computer Systems (APOCS)The Read-Only Semi-External ModelGuy E. Blelloch, Laxman Dhulipala, Phillip B. Gibbons, Yan Gu, Charles McGuffey, and Julian ShunGuy E. Blelloch, Laxman Dhulipala, Phillip B. Gibbons, Yan Gu, Charles McGuffey, and Julian Shunpp.70 - 84Chapter DOI:https://doi.org/10.1137/1.9781611976489.6PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We introduce the Read-Only Semi-External (ROSE) Model for the design and analysis of algorithms on large graphs. As in the well-studied semi-external model for graph algorithms, we assume that the vertices but not the edges fit in a small fast (shared) random-access memory, the edges reside in an unbounded (shared) external memory, and transfers between the two memories are done in blocks of size B. A key difference in ROSE, however, is that the external memory can be read from but not written to. This difference is motivated by important practical considerations: because the graph is not modified, a single instance of the graph can be shared among parallel processors and even among unrelated concurrent graph algorithms without synchronization, that instance can be stored compressed without the need for re-compression, the graph can be accessed without cache coherence issues, and the wear-out problems of non-volatile memory, such as Optane NVRAM, can be avoided. Using ROSE, we analyze parallel algorithms (some existing, some new) for 18 fundamental graph problems. We show that these algorithms are work-efficient, highly parallel, and read the external memory using only a block-friendly (and compression-friendly) primitive: fetch all the edges for a given vertex. Analyzing the maximum times this primitive is called for any vertex yields an (often tight) bound on the (low) I/O cost of our algorithms. We present new, specially-designed ROSE algorithms for triangle counting, FRT trees, and strongly connected components, devising new parallel algorithm techniques for ROSE and beyond. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-648-9 https://doi.org/10.1137/1.9781611976489Book Series Name:ProceedingsBook Code:APOSC21Book Pages:ii + 158
An optimized design for a field-of-view splitting-based long-gauge-length optical extensometer using two rhombic prisms

Abstract Although the field-of-view (FOV) splitting strategy, based on the reflective image technique, can effectively enhance the gauge length of an optical extensometer, its elements, including a right-angled optical prism and two plane mirrors, require meticulous adjustment before a measurement. In this research, we propose an optimized FOV splitting scheme that uses two identical rhombic prisms. Each rhombic prism can displace a beam of light without changing its direction. Therefore, two symmetrically positioned rhombic prisms converge the light that traverse from two separate regions of an object, which realizes the FOV splitting and greatly simplifies the adjustment of the optical elements. Based on such a FOV splitting scheme, we develop an enhanced optical extensometer. Repeated tensile and cyclic loading-unloading tests are conducted to validate the feasibility and reliability of the proposed extensometer. The experimental results that are obtained using the proposed method are in excellent agreement with those using a strain gauge, which indicates that the optimized FOV splitting scheme is easy to implement and it has high assembly accuracy. Finally, a tensile failure test of a cast-iron specimen is conducted; the measured results indicate that the proposed method is appropriate for measuring the strain of brittle materials in practical applications.
Experimental Evaluation of a Spectral Index to Characterize Temporal Variations in the Direct Normal Irradiance Spectrum

A simple index is desirable to assess the effects on both flat-plate and concentrating photovoltaics of natural changes in the solar spectrum. Some studies have suggested that the relationship between the Average Photon Energy (APE) and the shape of individual global tilted irradiance, global horizontal irradiance, or direct normal irradiance (DNI) spectra is bijective and can therefore be used as a single number to unequivocally replace a complete spectral distribution. This paper reevaluates these studies with a modified methodology to assess whether a one-to-one relationship really exists between APE and spectral DNI. A 12-month dataset collected in Jaén (Spain) using a sun-tracking spectroradiometer provides the necessary spectral DNI data between 350 and 1050 nm. After quality control and filtering, 78,772 valid spectra were analyzed. The methodology is based on a statistical analysis of the spectral distributions binned in 0.02-eV APE intervals, from 1.74 to 1.90 eV. For each interval, both the standard deviation and coefficient of variation (CV) are determined across all 50-nm bands into which the 350–1050-nm waveband is divided. CV stays below 3.5% within the 450–900-nm interval but rises up to 13% outside of it. It is concluded that APE may be approximately assumed to uniquely characterize the DNI spectrum distribution for Jaén (and presumably for locations with similar climates) only over the limited 450–900-nm waveband.