Reviewerly/exHarmony huggingface dataset:
Trifocal Relative Pose From Lines at Points

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and the novel case of (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Gröbner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver framework MINUS, which dramatically speeds up previous HC solving by specializing hc methods to generic cases of our problems. We characterize their number of solutions and show with simulated experiments that our solvers are numerically robust and stable under image noise, a key contribution given the borderline intractable degree of nonlinearity of trinocular constraints. We show in real experiments that (i) sift feature location and orientation provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches, where the state of the art structure from motion initialization fails.
Short-term load forecasting of power system based on genetic algorithm improved BP neural network algorithm

Forecasting method is the key in short-term load forecasting. Genetic algorithm is used in this paper to improve the BP neural network short-term load prediction model of power system, and the BP neural network model of hidden layer 10 is optimized and improved by genetic algorithm, so as to solve the problem that the prediction effect is not ideal due to excessive internal optimization of the model. The 24 o ’clock historical load, the meteorological characteristics of the day, the maximum and minimum temperature and other information were input into the prediction model as input variables. Finally, the genetic algorithm was used to get the prediction results of power load in different time periods and took them as output variables. In view of the proportion and threshold initialization problems in the model, and I use the genetic algorithm to optimize BP network. According to the daily meteorological characteristics, this paper uses genetic algorithm to improve BP network. prediction model. In the empirical study, our prediction results also meet the expected requirements, which fully shows that genetic algorithm is used to improve the prediction accuracy of BP network in this paper.
SOA-REAM Assisted Synaptic Receptor for Weighted-Sum Detection of Multiple Inputs

Neuromorphic photonics is a promising research field due to its potential to tackle the limitations arising from the bottleneck of the von-Neumann computation architecture. Inspired by the characteristics and behavior of the biological brain, photonic neural networks are touted as a solution for solving complex problems that require GHz operation at low latency and low power consumption. An essential building block of such a neural network is a low-complexity multiply-accumulate operation, for which efficient functional implementations in the optical domain are sought for. Towards this direction, we present a synaptic receptor that functionally integrates weighting and signal detection. This optical multiply-accumulate operation is accomplished through a monolithic integrated semiconductor optical amplifier and reflective electro-absorption modulator, which together serve as a colorless frequency demodulator and detector of frequency-coded signals. Moreover, we show that two spike trains can be simultaneously processed with alternating signs and detected as a weighted sum. The performance of the proposed synaptic receptors is further validated through a low bit error ratio for signal rates of up to 10 Gb/s.
Ensemble of Machine Learning Fusion Models for Breast Cancer Detection Based on the Regression Model

Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.
Interactive Digital Catalog for Canopy Workshop Using Augmented Reality

This research study develops a product promotion method for a canopy roofs. The development of this method is to apply a 3-dimensional (3D) catalog using Augmented Reality (AR) technology. By utilizing Augmented Reality technology, sellers do not need to create markets or miniature products that are commonly used to provide examples to consumers to save costs, attract consumer interest, and display objects that appear natural. Based on the tests that have been done, it is concluded that implementing Augmented Reality in the canopy sales promotion media using the Luther development method with the stages of analysis, design, implementation, testing, and maintenance. Implementation of Augmented Reality in canopy sales promotion media uses concept data from the types of canopies included in the Augmented Reality-based application, namely stainless and hollow types made using a 3D blender program. A marker as a sign to bring up 3D objects in the application. Markers are created using Photoshop and entered the database so that they can be stored online. System testing uses the BlackBox testing method where the program's functionality is running as desired.
Real-time performance analysis of distributed multithreaded applications in a cluster of ARM-based embedded devices

The challenges in real-time cluster computing, particularly in computing efficiency and reliability, have evolved significantly due to the increase of Internet of Things (IoT), cloud and edge computing applications. Lately, a number of low-power and low-cost clusters have appeared, based upon single board computers, which deploy multithreading techniques to run in parallel thousands of tasks, to support the ever-increasing demand for timely data processing. However, their real-time performance is still under research. This paper proposes a real-time performance analysis approach for evaluating metrics such as thread execution time, response time, parallel efficiency and speedup. The measurements are based upon recursively generated multithreaded applications running in parallel and distributed across multiple cores within a cluster of Raspberry Pi4, running Linux with real-time support and interconnected on a fast gigabit Ethernet. The experimental results validate the efficiency of the proposed approach and show that real-time support enables higher throughput across all workloads, and lower execution times.
CLFM: Few-Shot Object Detection via Low-Resource Contrastive Learning and Fisher Matrix Updating for Overcoming Catastrophic Forgetting

Few-shot object detection (FSOD) aims to efficiently detect novel instances by model transferring using a few novel-class samples after the base-class samples are pre-trained.However, catastrophic forgetting occurs when FSOD transfers to the novel-classes, making the transferred model unable to accurately detect base and novel class instances simultaneously.Thus, this paper attempts to extend the general elastic weight curing (EWC) to the field of few-shot transfer detection.An online soft constraint is applied by evaluating the Fisher information matrix of inner-batch samples based on mean squared error (MSE) metric to constrain or encourage model parameters transferring.Also, a momentum update based inter-batch storage mechanism is proposed to alleviate the memory strain caused by the previously applied contrastive learning modules when performing numerous contrastive encoding procedures.Finally, a compact heterogeneous decoupled-head is formulated to remove information entanglement between regression and classification tasks.The experimental results validate that on the PASCAL VOC and COCO datasets, compared with the FSOD baselines of original contrastive learning, the proposed method improves the average accuracy of the novel and the base classes by 1.3%∼15.1% and 7% separately, both of which reach the SOTA level.
POLITENESS STRATEGIES IN STUDENT`S LANGUAGE OF AS`ADIYAH ERENG ISLAMIC BOARDING SCHOOL, BANTAENG DISTRICT

Language has the important role in the process of communication in the community. Language politeness is the most appropriate thing to use in communication, because with polite language a person is able to maintain the value and dignity of himself and others. The use of polite language needs to be improved, especially among students who are in the public spotlight that students when interacting prioritize language politeness. In interaction and communication, politeness is one of the important aspects to be raised. Politeness aims to create good relationships and communication in social interaction between speakers and addressees. Both are intended to generate respect for oneself and others, so that the level of politeness will affect the values ​​and perspectives of others towards him. In addition, people will further strengthen the relationship between two. Islamic boarding school is a multilingual society. The diversity of language used by the students is clearly visible when communicating orally. This diversity occurs due to differences in the background of each student. Therefore, researchers are interested in conducting research in Islamic boarding schools. This research is entitled "Politeness Strategies in Students Language Of As`adiyah Ereng Islamic Boarding School, Bantaeng Regency".&#x0D; This study aims to describe forms of language politeness strategy for elevent graders in interaction in surroundings of As`adiyah ereng Islamic boarding school Bantaeng Regency in forman situation and nonformal situation using the perspective or theory of Brown Levinson (1987). This type of research is a type of qualitative research with a descriptive design. The technique used in collecting data in this study was carried out with observation techniques, recording techniques, and note-taking techniques. Data analysis through interpretation.&#x0D; The results of this study indicate that there are (7) bald on-record strategies, (12) positive politeness strategies, (4) negative politeness strategies, (1) off-record strategies, in this study there are also (3) silent strategies, the conclusion of this research is bald on-record as a politeness strategy most often used by students in both formal and non-formal situations. As a suggestion based on this research, it is expected that students pay attention to politeness in language so that they reflect polite language. For further researchers, this thesis can be used as an additional reference for further research.
VegaFusion: Automatic Server-Side Scaling for Interactive Vega Visualizations

The Vega grammar has been broadly adopted by a growing ecosys-tem of browser-based visualization tools. However, the reference Vega renderer does not scale well to large datasets (e.g., millions of rows or hundreds of megabytes) because it requires the entire dataset to be loaded into browser memory. We introduce VegaFusion, which brings automatic server-side scaling to the Vega ecosystem. VegaFusion accepts generic Vega specifications and partitions the required computation between the client and an out-of-browser, natively-compiled server-side process. Large datasets can be pro-cessed server-side to avoid loading them into the browser and to take advantage of multi-threading, more powerful server hardware and caching. We demonstrate how VegaFusion can be integrated into the existing Vega ecosystem, and show that VegaFusion greatly outperforms the reference implementation. We demonstrate these benefits with VegaFusion running on the same machine as the client as well as on a remote machine.
Practical web security testing: Evolution of web application modules and open source testing tools

Web application security testing is vital for preventing any security flaws in the design of web applications. A major challenge in web security testing is the continuous change and evolution of web design tools and modules. As such, most open source tools may not be up to date with catching up with recent technologies. In this paper, we reported our effort and experience testing our recently developed website (https://mysmartsa.com/). We utilized and reported vulnerabilities from several open-source security testing tools. We also reported efforts to debug and fix those security issues throughout the development process.
P-Hacking, Data Type and Data-Sharing Policy

In this paper, we examine the relationship between p-hacking and data-sharing policies for published articles. We collect 38,876 test statistics from 1,106 articles published in leading economic journals between 20022020. While a data-sharing policy increases the provision of research data to the community, we find a well-estimated null effect that requiring authors to share their data at the time of publication does not alter the presence of p-hacking. Similarly, articles that use hard-to-access administrative data or third-party surveys, as compared to those that use easier-to-access (e.g., own-collected) data are not different in their p-hacking extent. Voluntary provision of data by authors on their homepages offers no evidence of reduced p-hacking.
Batch-Efficient EigenDecomposition for Small and Medium Matrices

EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in the deep neural networks. In this paper, we propose a QR-based ED method dedicated to the application scenarios of computer vision. Our proposed method performs the ED entirely by batched matrix/vector multiplication, which processes all the matrices simultaneously and thus fully utilizes the power of GPUs. Our technique is based on the explicit QR iterations by Givens rotation with double Wilkinson shifts. With several acceleration techniques, the time complexity of QR iterations is reduced from $$O{(}n^5{)}$$ to $$O{(}n^3{)}$$ . The numerical test shows that for small and medium batched matrices (e.g., $$dim{<}32$$ ) our method can be much faster than the Pytorch SVD function. Experimental results on visual recognition and image generation demonstrate that our methods also achieve competitive performances.
Assessing Satisfaction in and Understanding of a Collaborative Explainable AI (Cxai) System through User Studies

Modern artificial intelligence (AI) and machine learning (ML) systems have become more capable and more widely used, but often involve underlying processes their users do not understand and may not trust. Some researchers have addressed this by developing algorithms that help explain the workings of the system using ‘Explainable’ AI algorithms (XAI), but these have not always been successful in improving their understanding. Alternatively, collaborative user-driven explanations may address the needs of users, augmenting or replacing algorithmic explanations. We evaluate one such approach called “collaborative explainable AI” (CXAI). Across two experiments, we examined CXAI to assess whether users’ mental models, performance, and satisfaction improved with access to user-generated explanations. Results showed that collaborative explanations afforded users a better understanding of and satisfaction with the system than users without access to the explanations, suggesting that a CXAI system may provide a useful support that more dominant XAI approaches do not.
Student’s Performance Prediction based on Personality Traits and Intelligence Quotient using Machine Learning

Apparently, most life activities that people perform depend on their unique characteristics.Personal characteristics vary across people, so they perform tasks in different ways based on their skills.People have different mental, psychological, and behavioral features that affect most life activities.This is the same case with students at various educational levels.Students have different features that affect their academic performance.The academic score is the main indicator of the student's performance.However, other factors such as personality features, intelligence level, and basic personal data can have a great influence on the student's performance.This means that the academic score is not the only indicator that can be used in predicting students' performance.Consequently, an approach based on personal data, personality features, and intelligence quotient is proposed to predict the performance of university undergraduates.Five machine learning techniques were used in the proposed approach.In order to evaluate the performance of the proposed approach, a real student's dataset was used, and various performance measures were computed.Several experiments were performed to determine the impact of various features on the student's performance.The proposed approach gave promising results when tested on the dataset.
Engineering-oriented approach for effective industrial and educational communications in the development of new master's programs

The article deals with the problems of the Russian engineering school. The needs of employers in terms of the formation of the necessary competencies have been investigated. The relevance of business communications in engineering education has been revealed. The main instruments of scientific and technological development and support of cooperation of enterprises, scientific and educational organizations and business are presented. Practice-oriented approach, project activities have been considered. An overview of new master's programs "Software Engineering and Cybernetics" and "Applied Systems Analysis" is presented.
SHARING RHYMES: FIVE BABY OWLS

Nursery WorldVol. 2020, No. 15 Nursery RhymesSHARING RHYMES: FIVE BABY OWLSPenny TassoniPenny TassoniSearch for more papers by this authorPenny TassoniPublished Online:3 Dec 2020https://doi.org/10.12968/nuwa.2020.15.26AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 December 2020Volume 2020Issue 15ISSN (print): 0029-6422ISSN (online): 2059-9358 Metrics History Published online 3 December 2020 Published in print 2 December 2020 Information© MA Education LimitedPDF download
A Quantitative Study of Impact of Incentive to Quality of Software Reviews

Online reviews like product reviews are important references for a potential user to learn the basic information of a product. However, some reviewers were rewarded for writing the reviews, which may impact the objectiveness of the reviews. But on the other hand, the reviews written by reviewers who weren't rewarded could be in low quality. Research study already showed incentivized reviewers may give a higher overall score than non-incentivized reviewers (or called organic) do, but does that pattern also apply to review content? In this paper, a quantitative comparison study is conducted to investigate the differences between the incentivized reviews and organic reviews of software products. Four pairs of comparison including overall score, sentiment preference, correlation, and similarity are performed by using statistical and text mining methods. The results show there is no statistically significant difference between the incentive reviews and organic reviews except the sentiment of total, “Problems and Benefits” and “Summary” part of a review text. The results are unexpected since the reviews collected from the website for reviewing software product already filtered low quality reviews. It demonstrates that the incentivized action might not be necessary to produce biased reviews and it may be an effective way to attract more reviews since the website include more than 75% incentivized reviews. The paper also analyzed the possible reasons from the feature of reviewers' position in a company, a review's indicator, and reviewers' common actions. Based on the analysis, this study suggests that a potential user may pay attention to some quality dimensions of a review to mitigate the bias risk from the reviews.
Design of high automatic target recognition unmanned reconnaissance system based on YOLOv5

This essay analyzes the defects of target recognition in the field of unmanned reconnaissance, and gives an optimization strategy based on YOLOv5 algorithm. The feasibility of this system is verified by simulation experiment and real machine test, which has certain help to the battlefield unmanned reconnaissance. Then, the simulation experiment of unmanned reconnaissance system target detection and recognition was carried out, which mainly collected and established target data sets of armored vehicles, helicopters and people, and completed target recognition model training. After that, the parameters of the training results were analyzed, and the weights obtained from the training were tested. The algorithm was optimized and improved according to the characteristics of the uav targets in the ultra-low altitude perspective, and the model training and testing were carried out for many times, which improved the recognition rate of small and medium targets in the actual scene.