Reviewerly/exHarmony huggingface dataset:
A Penetration Path Planning Algorithm for UAV Based on Laguerre Diagram

Due to the special requirements of modern military, unmanned combat aircraft (UAVs) attracts more and more attention from the researchers all over the world. It possesses many advantages, such as zero life risk, stronger combat capability ,and better adaptability to harsh combat environments than manual-controlled aircraft. The wide application of UAVs on the battlefield determines their important position in the war. With the rapid development of military science and technology. Nevertheless, there is a conflict between large amount of computing data in complex battlefield environments and limited computing capability of onboard computers. Therefore, a stable and efficient path planning algorithm is critical. This paper aims to dramatically improve the efficiency of path planning by using Laguerre diagram method. Through combing the Laguerre diagram method and the Dijkstra algorithm, the path planning algorithm we proposed in this paper could obtain feasible solutions with lower computational cost by decreasing path nodes and reducing the scale of the problem further. Furthermore, the current research mainly focuses on 2D scenes, which is inconsistent with the actual UAV flight situation. Therefore, this paper proposes a Laguerre diagram based path planning algorithm in 3D scenes. Simulation shows that the execution time of the stage of path planning reduced to one-third compare to the Dijkstra algorithm on the grid.
Time Interval Aware Collaborative Sequential Recommendation with Self-supervised Learning

Over the last few years, sequential recommender systems have achieved a great success in different applications. In the literature, it is generally believed that items farther away from the recommendation time have a weaker impact on the recommendation results. However, simply considering the distance between the interaction time and the recommendation time would prevent effective user representations. To solve this issue, we propose a Time Interval Aware Collaborative (TIAC) model with self-supervised learning for sequential recommendation. We propose to adjust the attention score learned from the time interval between an interaction time and the recommendation time using a time kernel function to achieve a better user representation. We also introduce self-supervised learning to combine the collaborative information obtained from a graph convolutional network and the sequential information learned from gated recurrent units to further enrich user representation. Extensive experiments on four real-world benchmark datasets show that our proposed TIAC model consistently outperforms state-of-the-art models under various evaluation metrics.
MORO: A Multi-behavior Graph Contrast Network for Recommendation

Multi-behavior recommendation models exploit diverse behaviors of users (e.g., page view, add-to-cart, and purchase) and successfully alleviate the data sparsity and cold-start problems faced by classical recommendation methods. In real-world scenarios, the interactive behaviors between users and items are often complex and highly dependent. Existing multi-behavior recommendation models do not fully utilize multi-behavior information in the following two aspects: (1) The diversity of user behavior resulting from the individualization of users’ intents. (2) The loss of user multi-behavior information due to inappropriate information fusion. To fill this gap, we hereby propose a multi-behavior graph contrast network (MORO). Firstly, MORO constructs multiple behavior representations of users from different behavior graphs and aggregate these representations based on behavior intents of each user. Secondly, MORO develops a contrast enhancement module to capture information of high-order heterogeneous paths and reduce information loss. Extensive experiments on three real-world datasets show that MORO outperforms state-of-the-art baselines. Furthermore, the preference analysis implies that MORO can accurately model user multi-behavior preferences.
Spline-Based Dense Medial Descriptors for Image Simplification Using Saliency Maps

Medial descriptors have attracted increasing interest in image representation, simplification, and compression. Recently, such descriptors have been separately used to (a) increase the local quality of representing salient features in an image and (b) globally compress an entire image via a B-spline encoding. To date, the two desiderates, (a) high local quality and (b) high overall compression of images, have not been addressed by a single medial method. We achieve this integration by presenting Spatial Saliency Spline Dense Medial Descriptors (3S-DMD) for saliency-aware image simplification-and-compression. Our method significantly improves the trade-off between compression and image quality of earlier medial-based methods while keeping perceptually salient features. We also demonstrate the added-value of user-designed, as compared to automatically-computed, saliency maps. We show that our method achieves both higher compression and better quality than JPEG for a broad range of images and, for specific image types, yields higher compression and similar quality than JPEG 2000.
Global-first Training Strategy with Convolutional Neural Networks to Improve Scale Invariance

Modelled closely on the feedforward conical structure of the primate vision system - Convolutional Neural Networks (CNNs) learn by adopting a local to global feature extraction strategy. This makes them view-specific models and results in poor invariance encoding within its learnt weights to adequately identify objects whose appearance is altered by various transformations such as rotations, translations, and scale. Recent physiological studies reveal the visual system first views the scene globally for subsequent processing in its ventral stream leading to a global-first response strategy in its recognition function. Conventional CNNs generally use small filters, thus losing the global view of the image. A trainable module proposed by Kumar & Sharma [24] called Stacked Filters Convolution (SFC) models this approach by using a pyramid of large multi-scale filters to extract features from wider areas of the image, which is then trained by a normal CNN. The end-to-end model is referred to as Stacked Filter CNN (SFCNN). In addition to improved test results, SFCNN showed promising results on scale invariance classification. The experiments, however, were performed on small resolution datasets and small CNN as backbone. In this paper, we extend this work and test SFC integrated with the VGG16 network on larger resolution datasets for scale invariance classification. Our results confirm the integration of SFC, and standard CNN also shows promising results on scale invariance on large resolution datasets.
Educational Opportunities

Journal AWWAVolume 115, Issue 1 p. 81-81 Educational Opportunities Educational Opportunities First published: 23 January 2023 https://doi.org/10.1002/awwa.2042Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume115, Issue1January/February 2023Pages 81-81 RelatedInformation
People in the News

Journal AWWAVolume 115, Issue 1 p. 80-80 People in the News People in the News First published: 23 January 2023 https://doi.org/10.1002/awwa.2041Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume115, Issue1January/February 2023Pages 80-80 RelatedInformation
A University Museum as a Tool of Students’ Vocational Skills Formation

The article analyzes the role of a university museum in the formation of students’ vocational skills. The author highlights that it is essential that students should solve pedagogical problems which are equivalent to those they will have to solve in real life. Applied tasks should be relevant and enable students to apply their professional knowledge in real-world environments. The article maintains that a university museum can help solve the aforementioned task. The author singles out a range of varied activities that enable students to accumulate sufficient applied experience: hosting excursions, providing master-classes on interactive museum software to schoolchildren, designing leaflets and display stands. Such activities enable students to gain initial vocational experiences and collect their portfolios which can later come in handy during the job-hunting process. To illustrate such activities, the article describes work of volunteer tour guides of the Time Machine team of Ryazan State University named for S. Yesenin, outlines the principles of their performance and the structure of the team.
Influence of Mathematical Anxiety on the Psychological Phenomenon of Cognitive Dissonance among Students Who Do Not Specialize in Mathematics

Students of non-mathematical specialties experience many problems when studying mathematics. This fact implicitly encourages teachers of higher mathematics to turn to psychology and the phenomenon of cognitive dissonance. In the philosophy of mathematical education, the problems of cognitive learning of understood mathematics are solved differently in accordance with cultural and educational traditions. Educational practice shows that mathematical anxiety is an important problem of learning, along with the formation of mathematical thinking.
Explanation Seeking and Recommendation Adherence in Human-to-Human versus Human-to-Artificial Intelligence Interactions

We conduct a lab experiment to examine when and why do explanation seeking and recommendation adherence behaviours differ in human-to-human versus human-to-AI settings and across variances in task certainty and deviation from prior belief. We find that individuals seek explanations more often from humans than AI models in unfavourable and abnormal conditions, and that recommendation adherence also differs between humans versus AI, tied to the salience of factors that influence the recommendation outcome, with higher levels of confidence associated with AI in unfavourable conditions. When interacting with humans, a favourable highly salient factor results in greater adherence, especially when accompanied by an explanation. However, when interacting with AI, greater adherence occurs despite the highly salient factor being unfavourable. We find that explanations matter for recommendation adherence, but only when the recommendation is favourable, unexpected, and from a human. Lastly, we find that trust is more frequently associated with AI over humans, particularly in unfavourable and abnormal conditions. We support our experimental findings with a posthoc analysis of participant comments using machine learning topic modelling, emotion classification, predictive modelling, and qualitative analysis.
Analysis of earthquake activity level in several districts in the Province of Aceh with using the Guttenberg-Richter method approach

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Irfandi Wally, Henry Junus Wattimanela, Yopi Andry Lesnussa; Analysis of earthquake activity level in several districts in the Province of Aceh with using the Guttenberg-Richter method approach. AIP Conference Proceedings 23 January 2023; 2588 (1): 050017. https://doi.org/10.1063/5.0112463 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
User scenario 2 seq: Automated system to generate sequence diagram from user stories scenario

Generating sequence diagrams from user stories scenario are needed to support improved software developments process. This paper proposed the process innovation of generating sequence diagrams from user stories scenario. The input of this system is user story scenario in form of semi structured format, we constructed User story scenarios adopting from Behat cheat sheet format. The user story scenario is processed by applying natural language processing to identify sequence diagram elements. The output of the system is sequence diagram in form of text code of Plant UML and portable network graphics (PNG) image format. To evaluate the result of study, we are comparing output of the system with manually generated by the groups of system analyst to calculate the accuracy of object creation, message flow, and name of the object identification. The system analysts are asked to create sequence diagram from user story scenario based on predefine rule. The accuracy of object creation, message flow, and name of the object identification respectively are 100%, 100%, and 40%. This application has successfully generated a sequence diagram for knowledge sharing by extracting the control objects, boundary objects, roles, messages, and message flows from the given, when, and then conditions and user stories in the user story scenario.
Cloud Computing

This technical note provides a general overview of cloud computing and how businesses are using it to achieve their digital goals. Specifically, this note outlines the different service models that cloud computing supports, including Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. The note also details some of the advantages and disadvantages of cloud computing, and it reviews the cloud computing industry and some of its top players.
An Error Analysis on the Use of English made by Local Tourism Workers of Jatiluwih Tourism Destination at Tabanan Bali

The research in general aimed on analyzing English language errors made by local tourism workers of Jatiluwih tourism destination in Tabanan, Bali, and specifically it aimed in revealing and analyzing types of errors made by the local tourism workers of Jatiluwih Tourism Destination in using English with foreign tourists. The primary research data are directly collected from the English language expressions used by the local tourism workers as informan of Jatiluwih Tourism Destination during their communication activities with the foreign tourists. The data informan are selected using purposive, snowball and abundant sampling techniques with the total number of 30 samples of the local tourism workers of Jatiluwih Tourism Destination in Tabanan, Bali. The collected data are analyzed using Error Analysis Theory proposed by Tarigan (1985) and Ellis (1986) (cited in Tarigan and Tarigan, 2011:63-64). The research reveal that the English language errors committed by local tourism workers of Jatiluwih Tourism Destination i Tabanan, Bali can be categorized into two major errors, which are: (1) errors of linguistic taxonomy category, which consists of two errors types, such as : (a) morphology errors and, (b) syntactic errors; (2) Errors of surface strategy taxonomy.
Mechanical Model and Knowledge Retrieval System Based on JsonLogic

This paper implements a mechanical model and a knowledge retrieval system based on JsonLogic for knowledge derivation. While using jieba word segmentation for Chinese word segmentation, a custom dictionary is added for a large number of unregistered words in the mechanical field to improve word segmentation accuracy. The concept of mechanical parts classification symbols is proposed, and the mechanical parts classification symbols are created to facilitate the retrieval of parts models, while ignoring the differences between real languages, so that people who do not understand Chinese can use the system to retrieve models. Through the derivation of JsonLogic, the model and knowledge expected by the user can be retrieved more accurately, so as to achieve the purpose of knowledge reuse.
Two-step Planning of Dynamic UAV Trajectories Using Iterative $$\delta $$-Spaces

UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal, however. On the other hand, direct planning in higher-dimensional state spaces generates globally optimal solutions but is time-consuming and thus infeasible for time-constrained applications. To address this issue, we propose $$\delta $$ -Spaces, a pruned high-dimensional state space representation for trajectory refinement. It does not only contain the area around a single lower-dimensional path but consists of the union of multiple near-optimal paths. Thus, it is less prone to local minima. Furthermore, we propose an anytime algorithm using $$\delta $$ -Spaces of increasing sizes. We compare our method against state-of-the-art search-based trajectory planning methods and evaluate it in 2D and 3D environments to generate second-order and third-order UAV trajectories.
Few-shot Learning for Co-segmentation

This chapter describes a few-shot learning approach to tackle the small sample size problem encountered in learning co-segmentation models with small datasets like iCoseg and MSRC. The multi-image co-segmentation algorithm explained in this chapter involves a class agnostic meta-learning strategy by generalizing the model to new classes given only a small number of training samples for each new class. This is achieved through a directed variational inference cross-encoder, which learns a continuous embedding space to ensure better similarity learning.
Introduction

If one is given several images sourced from various places and under different contexts, but having at least ‘something’ in common, then the extraction of this common object in all these images is known as image co-segmentation. It is the problem of segmenting objects with similar features from more than one image. In this monograph, we discuss efficient solutions to this problem ensuring robustness and high accuracy and provide theoretical analysis for the same.
Building a Model of Wind Turbine Power Using AutoML Methods

Wind power is one of the prominent alternative sources of energy. But due to its unstable nature it is crucial to be able to predict amount of energy being generated. To this end construction of an appropriate power model is needed. Various approaches can be used to deal with this challenge, from equation build upon physical approximations and up to neural network models. In this work we show the applicability of AutoML methods for this task. Automated Machine Learning is a group of methods that ensure search for the most suitable combination of machine learning algorithm and its hyperparameters that is able to deal with the given dataset. Using Wind Turbine Scada Dataset we explore some commonly used AutoML tools to find the most appropriate algorithm. The results of a selected algorithm predictions then compared to the theoretical power values supplied by turbine manufacturer that are built upon approximation of generated energy based on weather conditions.
Efficient Shapely Explanation of Support Vector Regression for Agile and Non-agile Software Effort Estimation

There is evidence that precise prediction of the software development effort plays a crucial role in properly monitoring and managing software projects. Researchers have suggested numerous techniques for accurately estimating software effort. However, to date, no large-scale studies have been undertaken to examine the prevalence of interpretability of estimates. In particular, in the literature, the support vector regression model has shown good performance in estimating both agile and non-agile software development costs. Despite its remarkable outperformance, this black-box model needs to be interpreted using several model-agnostic explanation techniques. This study aims to explain the convenience of model-agnostic methods in interpreting the estimated effort using the SVR-RBF model optimized with an artificial immune network in an agile and non-agile context. The estimations are performed using two datasets, the leave-one-out cross-validation and 30% holdout methods. To help practitioners and managers trust and comprehend the SVR estimates, we employ four global (Feature Importance, Interaction, Dependence, and Summary Plot) and two local interpretability methods (Force and Waterfall Plot). The results show that global and local interpretability techniques effectively assist practitioners in decision-making.
Prediction of Communication Effectiveness Using Naïve Bayes Algorithm

Communication could be better throughout the world. It's hard to know or predict by the level of the individual as it requires expertise and higher prediction knowledge. The environment in the communication sector is information-rich, but it needs more understanding. A lot of data is available in communication systems over the internet, but there need to be more practical analysis tools to discover hidden talents among them. An automated or self-made system will increase communication efficiency and decrease time. This software's goal is to know or predict the level of communication based on the data collected from the individual. The Main aim is to extract the hidden talents to predict or understand the present value on a scale. This paper is based on the current trend that students move towards placement after graduation. The basic need for the interview is communication level. According to students, most of them need to improve in communication skills. This paper is used to develop and predict the outcome of the student. This can be a practice platform and also a testing platform. Most of the upcoming students need more communication skills. The very reason is fear of interacting with everyone. This problem happens when the skill needs to be improved. This paper gives a solution that the person can practice and predict their skills using this app. Our goal is to find a suitable technique that is efficient and accurate for the prediction of communication.
Mammograms enhancement based on multifractal measures for microcalcifications detection

Breast cancer is the most common cancer in women and represents as a leading cause of death in the world (https://gco.iarc.fr/today/home). Microcalcifications (MCs) are the essential signs of precancerous cells. Their small size makes them difficult to detect and locate, hence the need of developing computer-aided detection systems for early detection of breast cancer. In this paper, an approach of MCs detection is proposed. Our system includes three phases. In the first, we start by a pre-processing step to remove various noises, followed by a step of intensity enhancement based on the haze removal algorithm. The third step is based on multifractal measures to construct the α-image which enhance MCs contrast. The proposed method was tested on three databases with a set of 371 images and evaluated in terms of PSNR and sensitivity. The obtained results are very significant and better compared to other approaches proposed in the literature.