Reviewerly/exHarmony huggingface dataset:
Prediction model of disease and pest epidemic of jujube based on Internet of Things

To reduce the loss of the jujube production and develop more effective pest control measures, this paper studies the prediction model of jujube pest epidemic based on the Internet of Things technology. The data of jujube growth was collected based on the Internet of Things technology, and multi-source information was obtained by combining sensor technology and network communication technology. The characteristic bands of the jujube image were extracted. The useless bands were removed, and the overall physical information of the image was retained. The prediction model of the jujube pest and disease epidemic situation was established, and the prediction factors with poor stability were processed by BP neural network structure to realize the prediction. Through experimental demonstration and analysis, compared with the actual occurrence degree of diseases and insect pests, the prediction curve of the occurrence degree of diseases and insect pests of jujube by this model is consistent with the changing trend of the actual occurrence degree curve. Compared with the traditional model, the prediction error of the proposed model is smaller, which proves that the proposed model is more effective. In this paper, the model can obtain effective information on the basis of multi-source data, and get more accurate prediction results, which provides a scientific basis for decision-making for the formulation of pest and disease control plan to ensure the high and stable yield of jujube.
A Tropical Power Sum

SummaryWe prove several combinatorial identities related to the so-called “tropical” sums a⊕b=min(a,b).
Mineralogical and isotopic studies in the Gol-Cheshmeh copper deposit, NE Iran

&amp;lt;p&amp;gt;The studied area is situated as a narrow strip NE Iran and belongs to Central Iran structural zone and also the Kavir-Sabzevar magmatic zone which is related to &amp;lt;em&amp;gt;continental arc subduction zone of NE Iran. The mineralization is hosted in Eocene andesite-basalt, trachyandesite, andesite, and monzodiorite with high potassic shoshonite affinities that have undergone propylitic alteration. The &amp;lt;/em&amp;gt;&amp;lt;em&amp;gt;disseminated copper oxide and sulfide mineralization occurs in the form of stratabound, vein-veinlet and open space filling&amp;lt;/em&amp;gt;&amp;lt;em&amp;gt;,&amp;lt;/em&amp;gt;&amp;lt;em&amp;gt; &amp;lt;/em&amp;gt;&amp;lt;em&amp;gt;resulted from replacement of volcanic units simultaneously with sericitization and silicification of the host rocks. Two stages of mineralization have been recognized, namely a hydrothermal stage and supergene enrichment stage. Chalcocite, covellite, chalcopyrite, bornite, digenite, and tetrahedrite are the main hypogene minerals, that were altered during the supergene enrichment into bornite, secondary chalcocite, and Cu-carbonates. The range of &amp;lt;/em&amp;gt;&amp;amp;#948;&amp;lt;sup&amp;gt;34&amp;lt;/sup&amp;gt;S values in primary chalcocites (-7.7&amp;lt;em&amp;gt;&amp;amp;#8240;&amp;lt;/em&amp;gt;-9.3&amp;lt;em&amp;gt;&amp;amp;#8240;&amp;lt;/em&amp;gt;) and the &amp;amp;#948;&amp;lt;sup&amp;gt;34&amp;lt;/sup&amp;gt;S &amp;lt;sub&amp;gt;H2S &amp;lt;/sub&amp;gt;(-3.5&amp;lt;em&amp;gt;&amp;amp;#8240;-5.2&amp;amp;#8240;&amp;lt;/em&amp;gt;) indicate that the source of sulfur in ore forming fluids was most likely sedimentary. The hypogene ore minerals are enriched in Ag, As, Ba, Sr, Fe, Mn, Zn, and Ce compared to their supergene counterparts. The enrichment of Cu took place during the supergene stage. The study of hornblende pyroxene olivine andesite basaltic zircons, show the average age of 5.47 &amp;amp;#177; 0.22 my for this unit. Also, based on the study of zircons, the average age of the pyroxene hornblende monzodiorite is 2.44 &amp;amp;#177; 0.79 my.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Key words: &amp;lt;/strong&amp;gt;&amp;lt;em&amp;gt;Gol-Cheshmeh; &amp;lt;/em&amp;gt;&amp;lt;em&amp;gt;Manto-type; Cu mineralization; sulfur isotopes; Nd-Sr isotopes; NE Iran.&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Forschung: Vakuum in Forschung und Praxis 1/2023

Vakuum in Forschung und PraxisVolume 35, Issue 1 p. 14-22 Magazin Forschung: Vakuum in Forschung und Praxis 1/2023 First published: 22 February 2023 https://doi.org/10.1002/vipr.202370103AboutPDF 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 Volume35, Issue1February/March 2023Pages 14-22 RelatedInformation
Application exploration of building deep learning model by adjusting algorithm combination relationship

Abstract According to the Convention, the basic data type and XML representation algorithm are adopted, and after the organization is completed, it is compiled into Python code.According to the vectors in mathematics, whether the given algorithms share the same type of variables, the forward propagation and back propagation iterative algorithms in deep learning are used to train the model, and a new algorithm is combined according to the value of vectors, which is similar to the forward calculation in deep learning.Using the sample data to run the algorithm, the vector code is adjusted according to the error value in order to achieve convergence, which is similar to the reverse learning process in deep learning.In this way, it is expected to train a suitable model only by adjusting the parameter combination relationship of the algorithm sequence, and the neuron nodes and weight parameters are greatly reduced, which is expected to bring better learning effect.
A low-light image enhancement method based on AUDD-GAN

Abstract A large number of images taken under conditions of insufficient lighting, often with overall darkness, low contrast, noise and inconspicuous details, thus affecting people’s observation of the image content and the subsequent use of the image. This paper presents a novel method, Attention U-Net Dual Discriminator-Generative Adversarial Networks (AUDD-GAN), which combines attention mechanism, U-Net network structure and the dual discriminator. First, the method generates a normalized attention map to guide the subsequent modules to perform illumination enhancement to prevent overexposure or underexposure problems; second, the U-Net generative network extracts characteristics of diverse levels to acquire richer detail information; then, a dual discriminator is used as the discriminative network, where PatchGAN is used as the local discriminator to achieve better enhancement effects; ultimately, we combine multiple loss functions as the joint loss function. AUDD-GAN is verified to enhance the global contrast of the image while enhancing the detail features more effectively.
Rethinking Low-Level Features for Interest Point Detection and Description

Although great efforts have been made for interest point detection and description, the current learning-based methods that use high-level features from the higher layers of Convolutional Neural Networks (CNN) do not completely outperform the conventional methods. On the one hand, interest points are semantically ill-defined and high-level features that emphasize semantic information are not adequate to describe interest points; On the other hand, the existing methods using low-level information usually perform detection on multi-level feature maps, which is time consuming for real time applications. To address these problems, we propose a Low-level descriptor-Aware Network (LANet) for interest point detection and description in self-supervised learning. Specifically, the proposed LANet exploits the low-level features for interest point description while using high-level features for interest point detection. Experimental results demonstrate that LANet achieves state-of-the-art performance on the homography estimation benchmark. Notably, the proposed LANet is a front-end feature learning framework that can be deployed in downstream tasks that require interest points with high-quality descriptors. (Code is available on https://github.com/wangch-g/lanet .).
Gestalt-Guided Image Understanding for Few-Shot Learning

Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks. However, human can quickly learn the feature of a new category from very few samples. Nevertheless, previous work has rarely considered how to mimic human cognitive behavior and apply it to few-shot learning. This paper introduces Gestalt psychology to few-shot learning and proposes Gestalt-Guided Image Understanding, a plug-and-play method called GGIU. Referring to the principle of totality and the law of closure in Gestalt psychology, we design Totality-Guided Image Understanding and Closure-Guided Image Understanding to extract image features. After that, a feature estimation module is used to estimate the accurate features of images. Extensive experiments demonstrate that our method can improve the performance of existing models effectively and flexibly without retraining or fine-tuning. Our code is released on https://github.com/skingorz/GGIU .
Improving Surveillance Object Detection with Adaptive Omni-Attention over Both Inter-frame and Intra-frame Context

Surveillance object detection is a challenging and practical sub-branch of object detection. Factors such as lighting variations, smaller objects, and motion blur in video frames affect detection results, but on the other hand, the temporal information and stable background of a surveillance video are major advantages that does not exist in generic object detection. In this paper, we propose an adaptive omni-attention model for surveillance object detection, which effectively and efficiently integrates inter-frame contextual information to improve the detection of low-quality frames and intra-frame attention to suppress false positive detections in the background regions. In addition, the training of the proposed network can converge quickly with less epochs because during multi-frame fusion stage, the pre-trained weights of the single-frame network can be used to update simultaneously in reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed. (Code is available at https://github.com/Yubzsz/Omni-Attention-VOD .)
Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward

Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers. It mainly contains two stages: video segmentation and segment assemblage. The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage. To address these problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can perform efficient and coherent segment assemblage task end-to-end. It utilizes multi-modal representation extracted from the segments and follows the Encoder-Decoder Ptr-Net framework with the Attention mechanism. Importance-coherence reward is designed for training M-SAN. We experiment on the Ads-1k dataset with 1000+ videos under rich ad scenarios collected from advertisers. To evaluate the methods, we propose a unified metric, Imp-Coh@Time, which comprehensively assesses the importance, coherence, and duration of the outputs at the same time. Experimental results show that our method achieves better performance than random selection and the previous method on the metric. Ablation experiments further verify that multi-modal representation and importance-coherence reward significantly improve the performance. Ads-1k dataset is available at: https://github.com/yunlong10/Ads-1k .
Automatic Configuration of Metaheuristics for Solving the Quadratic Three-Dimensional Assignment Problem Using Irace

Metaheuristic algorithms are traditionally designed following a manual and iterative algorithm development process. The performance of these algorithms is, however, strongly dependent on their correct tuning, including their configuration and parametrization. This is labour-intensive, error-prone, difficult to reproduce and explores only a limited number of design alternatives. To overcome manual tuning, the automatic configuration of algorithms is a technique that has shown its efficiency in finding performance-optimizing settings of parameters. This paper contributes to overcoming the challenge of automatically configured metaheuristics using the iterated racing for automatic algorithm configuration irace applied to the quadratic three-dimensional assignment problem. In particular, we use particle swarm optimization (PSO), a tabu search (TS), an iterated local search (ILS) and two hybrid algorithms PSO-TS and PSO-ILS. Of these algorithms, the tabu search algorithm and the PSO-ILS worked the best. The results show that the algorithm automatic configuration enables identifying an ideal tuning of the parameters and reaching better results when compared to a manual configuration, in similar execution time.
Decision-Based Black-Box Attack Specific to Large-Size Images

Decision-based black-box attacks can craft adversarial examples by only querying the target model for hard-label predictions. However, most existing methods are not efficient when attacking large-size images due to optimization difficulty in high-dimensional space, thus consuming lots of queries or obtaining relatively large perturbations. In this paper, we propose a novel decision-based black-box attack to generate adversarial examples, which is Specific to Large-size Image Attack (SLIA). We only perturb on the low-frequency component of discrete wavelet transform (DWT) of an image, reducing the dimension of the gradient to be estimated. Besides, when initializing the adversarial example of the untargeted attack, we remain the high-frequency components of the original image unchanged, and only update the low-frequency component with the randomly sampled uniform noise, thereby reducing the distortion at the beginning of the attack. Extensive experimental results demonstrate that the proposed SLIA outperforms state-of-the-art algorithms when attacking a variety of different threat models. The source code is publicly available at https://github.com/GZHU-DVL/SLIA .
RA Loss: Relation-Aware Loss for Robust Person Re-identification

Previous relation-based losses in person re-identification (ReID) typically comprise two sequential steps: they firstly sample both positive pair and negative pair and then deploy constraints to simultaneously improve intra-identity compactness and inter-identity separability. However, existing relation-based losses usually place emphasis on exploring the relation between images and therefore consider only several pairs during each optimization. This inevitably leads to different convergence status for pairs of the same kind and brings about the intra-pair variance problem. Accordingly, we propose a novel Relation-Aware (RA) loss to address the intra-pair variance via exploring the informative relation across pairs. In brief, we introduce a macro-constraint and a micro-constraint. The macro-constraint encourages the separation of positive pair and negative pair via pushing far apart the two “centers” of the positive pair and the negative pair. The “center” of each kind of pair are obtained via averaging all the pairs of the same kind. The micro-constraint further enhances the compactness by minimizing the discrepancies among pairs of the same kind. The two constraints work cooperatively to relieve the intra-pair variance and improve the quality of pedestriansąŕ representation. Results of extensive experiments on three widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID and CUHK03, demonstrate that the RA loss brings improvements over existing relation-based losses.
Towards User2Machine Model for Higher Education - Enforced by Covid19 Pandemic

The Covid19 pandemic hit the higher education system hard. Suddenly, universities around the world are obliged to offer online educational instructions to students. Innovative technologies made this transition possible, and new technologies have opened prospects, connected societies, and created new ways of communication based on the three models in which humans initially communicated: user2user, user2society, and user2society2user. Now, fostered by the covid19 pandemic and with advancements in technological innovations, “User2Machine”-interaction, as a model, has been introduced to online education. This model is helping in creating different methods of communication channels regardless of the time and location of the user. Eventually, this has resulted in forming new interaction modes in the virtual social space. In this paper the User2Machine-interaction model is proposed, focusing on technologies and functionalities affecting this model and contributing to changes in the way users interact with systems. Technologies such as Artificial Intelligence (AI), Virtual Reality (VR) and Chatbots are described to see where do they fit in the new innovative model, and how they affected online education and the ecology of higher education during and post covid19 pandemic.
Lightweight Image Matting via Efficient Non-local Guidance

Natural image matting aims to estimate the opacity of foreground objects. Most existing approaches involve prohibitive parameters, daunting computational complexity, and redundant dependency. In this paper, we propose a lightweight matting method termed LiteMatting, which learns the local smoothness of color space and affinities between neighboring pixels to estimate the alpha mattes. Specifically, a modified mobile block is adopted to construct an encoder-decoder framework, which reduces parameters while retaining sufficient spatial and channel information. In addition, a Long-Short Range Pyramid Pooling Module (LSRPPM) is introduced to extend the reception field by capturing long-range dependency between regions distributed discretely. Finally, an Efficient Non-Local Block (ENB) is presented for guiding high-level semantics propagation from low-level detail features to refine the alpha mattes. Extensive experiments demonstrate that our method achieves a favorable trade-off between accuracy and efficiency. Compared with most state-of-the-art approaches, our method attains an immense descent in parameters and FLOPs with 30 $$\%$$ and 13 $$\%$$ , respectively, while achieving an improvement of over 15 $$\%$$ in SAD metrics. Code and model are available at https://github.com/kzx2018/LiteMatting.
An Agent-Based Model of Follow-the-leader Search Using Multiple Leaders

In this paper we study a swarm optimisation algorithm for real-valued bound-constraint cost functions whose search strategy operates on the basis of follow-the-leader intensification and random walk diversification. We studied the single-leader and multi-leader modes of operation. The simplicity of the search rules allows for a straightforward implementation of the algorithm as an agent-based model. In addition, various techniques were devised to prevent premature convergence to local minima and stagnation. We evaluate the efficacy/efficiency of the algorithm with empirical experiments on a testbed of well-known unconstrained real-valued cost functions using the NetLogo simulation environment. Our results indicate that the multi-leader configuration, with a small number of followers, proved to be advantageous both in accelerating convergence to the optimum on all testbed problems and in improving success rates compared to its single-leader version.
Computer Aided Technologies in Learning Foreign Languages for Engineering Students

Currently, the teaching methods are constantly evolving in terms of education digitalization. There is a strong need for a qualitative transfer of the educational environment, its methods and tools into smart education. In the context of foreign language studies, this transition can be accomplished through the development and use of all opportunities of various language tools and digital technologies. In our study, we focus on the Web-based application Quizlet, a flash-card program considered a tool for mastering vocabulary in various fields of study. However, it offers various options for sharpening skills and they are not limited only to vocabulary skills development. Thus, this study aims to reveal the efficiency and feasibility of Quizlet for foreign language purposes of the engineering students.
Poster: Design Project in Kanazawa Technical College Implemented with Reference to Project Design Programs in Kanazawa Institute of Technology

Kanazawa Technical College (KTC) has also introduced PBL-based design projects, referring to the engineering design education at Kanazawa Institute of Technology (KIT). Since the curriculum of a college of technology differs greatly from that of a university and time is quite limited, it was necessary to devise a way to introduce PBL in the same way as at a university. Therefore, KTC’s design project was implemented with an appropriate project theme provided by a local printing company, and the quality of the student project deliverables was high and effective.
Students’ Feedback on Teaching and Learning English for Specific Purposes Before, During, and After the Covid-19 Pandemic

Due to the covid-19 pandemic, great changes appeared in education in general and digital technologies played a substantial role in the process. The main objective of this research is to consider students’ feedback and opinions on teaching and learning English for Specific Purposes (ESP) in (a) the present form before schools were closed due to the pandemic, (b) the online distance form of instruction during the pandemic, and (c) the present form of instruction after the pandemic. Research questions focused on three areas: (1) what form(s) of instruction students preferred, (2) whether/how digital technologies contributed to the process of instruction, and (3) in which form of instruction they learned more. The ex-post-facto approach and mixed methodology were applied. Data were collected by questionnaire. It was available online for students of the upper secondary school for medical staff (N = 153) and advanced school for higher medical staff (N = 183). We expect that (1) the present form of instruction after the covid-19 pandemic will be appreciated; however, there will also be some supporters of online distance instruction; (2) it will be the present form that will allow students to learn more compared to online distance instruction; (3) both teachers and students will improve their digital skills during the pandemic so that technologies can be exploited to a wider extent in the present form of instruction after the pandemic.