Reviewerly/exHarmony huggingface dataset:
Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm

Scientific and technological innovation is the source of the survival and development of enterprises and the key to the realization of the goal of prosperity. In recent years, more and more companies have begun to focus on technological innovation, but the results are not significant, so companies have begun to explore the factors that affect their technological innovation. The management is the helm of the development of the enterprise, the main body of the company’s actual production activities, and the direct person in charge of the company’s management. Its influence on the innovation of the enterprise is self-evident, and the education level of the management directly determines the manager’s ability and vision. However, the current research on management mainly focuses on the position change of management and the rights of management and does not involve the level of education of management. Based on this, this article started from the management education and subdivided it with the K-means clustering algorithm, so as to explore the impact of management education on the technological innovation of enterprises. The experiment showed that there was a significant positive correlation between the educational level of management and the technological innovation ability of enterprises, and the correlation coefficient was 1.521. It fully shows that the management with a higher education background will promote the enterprise to carry out scientific and technological innovation practice and continuously improve the enterprise’s innovation ability.
ANALISIS PROFIL JUDUL PENELITIAN DENGAN RIP LPPM UCY 2022-2027 STUDI PADA FE UCY

Research activities are one of the tridharma of higher education. For lecturers, this activity is a prerequisite in fulfilling the lecturer's performance load report which is reported every semester, while for students it is a prerequisite for the final project report in fulfilling graduation as a bachelor's degree. The problem is that although the productivity of lecturers' research through student guidance in completing their final assignments has increased, research products that have the potential to be published in national and international journals are still limited.&#x0D; This study aims to determine the profile of research titles for students of the Faculty of Economics, University of Cokroaminoto, Yogyakarta with the research themes in the Research Master Plan (RIP) LPPM UCY 2022-2027. According to the RIP LPPM 2020-2027, the leading themes of research and community service at UCY are set in 5 (five) areas of community life. The field of study is adjusted to the expertise of the study program at UCY. The leading research themes in each field are as follows: 1) Religion Religion: Strengthening Community Religiosity towards the welfare of the nation 2) Economics: Human Resource Management, Financial Management, Marketing and Poverty Alleviation, 3) Law: Strengthening local governance and protection of human rights , 4) Education: Development of IT-based education with Indonesian character 5) Engineering: Disaster Management Management.&#x0D; The results showed that there were 24 titles of student research results with the theme of economics, namely 20 from management study programs and 4 from accounting study programs. Of the 24 themes related to Human Resources there are 2 titles, Financial Management consists of 8 titles, Marketing consists of 14 titles and Poverty Alleviation is not yet available.&#x0D; &#x0D; &#x0D;
Global Context Aggregation Network for Temporal Action Proposal Generation

Temporal action proposal generation is an important yet challenging task which aims to locate action boundary in untrimmed long video. Existing methods usually generate precise action boundary with local context, but cannot generate reliable confidence scores for retrieving proposals. To address this difficulty, we aggregate global contextual information to extract rich features when evaluating proposal confidence score. In this paper, we propose Global Context Aggregation Network (GCA-Net) to generate temporal action proposals. The main advantage of GCA-Net lies in the fact that global context contains rich semantic and temporal information to improve the accuracy of proposal confidence score. Specifically, GCA-Net utilizes U-shaped architecture and RNN to extract uniformly fused global semantic features and bi-direction global temporal features respectively. Then proposal evaluation block is proposed to merge these global contextual features with local regular features to evaluate confidence scores of densely distributed proposals. Extensive experiments are conducted on two challenging datasets: THUMOS-14 and ActivityNet-1.3, where GCA-Net can generate proposals with high precision and recall. By combining with the existing action classifier, GCA-Net can obtain remarkable temporal action detection performance compared with other methods.
SIMULASI KESTABILAN PROTOTYPE UAV-SPRAYER BERBASIS QUADCOPTER TERHADAP PENAMBAHAN SEKAT PADA PENAMPUNG CAIRAN

The use of UAVs has begun to penetrate the world of agriculture. One of the functions of UAVs in agriculture is to spray pesticides. The pesticides used are liquid so that when the UAV is airborne and maneuvering, the fluid experiences fluid motion or sloshing. Sloshing that occurs can cause the balance of the UAV to be disturbed. To overcome this, a bulkhead or baffle is needed in the reservoir in order to reduce fluid movement. In the case of the research studied, the simulation of sloshing in the reservoir with the presence of baffles and without the presence of baffles. This research uses different reservoir variations and different water levels, namely 55 mm, 35 mm and 15 mm. Simulations are carried out during cruising and maneuvering flights at a speed of 2 m/s. The container modeling uses the Catia V5R20 software and the simulation uses the Ansys 14.5 software. The simulation results show that the effect of baffle placement is more visible if the baffle is placed in the xy plane, while for the baffle placement in the yz plane, the force caused by sloshing is greater. In spraying the UAV-Sprayer will more often fly forward (cruise), while for maneuvers (right or left) it is only done occasionally/not too often. So that giving baffles is more effective in the xy plane because it can reduce sloshing better than the baffles in the yz plane.
Interactive Causal Correlation Space Reshape for Multi-Label Classification

Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels.Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information of the label space.However, the label correlation matrix is usually directly added to the model, which ignores the interactive causality of the correlation between the labels.Considering the label-specific features based on the distance method merely may have the problem of distance measurement failure in the high-dimensional space, while based on the sparse weight matrix method may cause the problem that parameter is dependent on manual selection.Eventually, this leads to poor classifier performance.In addition, it is considered that logical labels cannot describe the importance of different labels and cannot fully express semantic information.Based on these, we propose an Interactive Causal Correlation Space Reshape for Multi-Label Classification (CCSRMC) algorithm.Firstly, the algorithm constructs the label propagation matrix using characteristic that similar instances can be linearly represented by each other.Secondly, label co-occurrence matrix is constructed by combining the conditional probability test method, which is based on the label propagation reshaping the label space to rich label semantics.Then the label co-occurrence matrix combines with the label correlation matrix to construct the label interactive causal correlation matrix to perform multi-label classification learning on the obtained numerical label matrix.Finally, the algorithm in this paper is compared with multiple advanced algorithms on multiple benchmark multi-label datasets.The results show that considering the interactive causal label correlation can reduce the redundant information in the model and improve the performance of the multi-label classifier.
A Method of the Coverage Ratio of Street Trees Based on Deep Learning

The street trees coverage ratio provides reliable data support for urban ecological environment assessment, which plays an important part in the ecological environment index calculation.Aiming at the statistical estimation of urban street trees coverage ratio, an integrated model based on YOLOv4 and Unet network for detecting and extracting street trees from remote sensing images is proposed, and obtain the estimated street trees coverage ratio in images accurately.The experiments are carried out under self-made dataset, and the results show that the accuracy of street trees detection is 94.91%, and the street trees coverage ratio is 16.30% and 13.81% in the two experimental urban scenes.The MIoU of contour extraction is 98.25%, and the estimated coverage accuracy is improved by 6.89% and 5.79%, respectively.The result indicates that the proposed model achieves the automation of contour extraction of street trees and more accurate estimation of street trees coverage ratio.
Multi-element quantitative extraction of mining area of laterite nickel mine combined deep learning with object-oriented method

Remote sensing technology has great advantages in timely and rapid monitoring of open-pit mining conditions. Based on Worldview-2 multi-spectral satellite remote sensing images, we took a typical lateritic nickel deposit in Indonesia as an example, and 9 types elements which are mining area, dump, collection sump, tailings pond, buildings, roads, smelter, vegetation and bare soil in mining active areas were extracted. Firstly, a deep learning method based on TensorFlow framework was used to extract the main roads and mining areas from the pre-processed images to obtain vector data. Secondly, according to the vector data, our study area can be divided into two areas, center and outskirts, by FNEA coarse segmentation, and the local variance change rates of the two areas are calculated, so as to select appropriate segmentation scales for each factor type and establish a bottom-up multi-scale segmentation hierarchy. Thirdly, the spectral difference index (SDI) and PCA-based GLCM texture features were proposed to expand the feature base. The FSO algorithm and SEaTH algorithm were combined to select the optimal features and separation thresholds. At last, the multi-element extraction of laterite nickel ore area was completed hierarchically. The overall accuracy reached 90.12&#x0025;. Our results indicated that the proposed method takes into account the spatial differences of various elements, ensuring the accuracy of segmentation and element extraction. Furthermore, method of selecting scales and thresholds avoids multiple experiments and reduces the time and labor cost of trial and error, which ensures objectivity and improves the selection efficiency. In addition, the PCA-based texture features can shorten the feature calculation time from 6.25 min to 14 s, reducing the operation time of the algorithm and greatly saving the operation time while ensuring the correlation effectively.
Information Extraction with Hierarchical Multi-scale Segmentation and Dynamic Change Analysis in Myanmar Tagaung Taung Nickel Mine

Recently, high spatial resolution remote sensing images have been widely used to classify in the mining area. However, the information extraction and dynamic change analysis of typical open-pit laterites nickel mine by using high spatial resolution remote sensing images is less studied. The openpit Tagaung Taung nickel mine is one of the famous laterites nickel ore in Myanmar. In this paper, based on the data of Pleiades and GF-2 image, we used the method of object-oriented classification with hierarchical multi-scale segmentation to extract ground object information and analyze the dynamic change in the Tagaung Taung nickel mine. The qualitatively and quantitatively relative analysis show that identification results with hierarchical multi-scale segmentation has better effect and the precise. The overall accuracies of Pleiades and GF-2 image are 94.24&#x0025; and 89.02&#x0025; respectively. Accordingly, this method is more suitable to be utilized to the information extraction of openpit laterites nickel mine. Through dynamic change analysis, the mining scale of Tagaung Taung Nickel mine continues to expand from 2015 to 2017, and the mining speed is fast. Consequently, the deposit has good resource development potential and broad development prospects in the future.
Developing awareness needs for students in universities in the process of implementing the "learning citizen" model in the context of globalization

The university is a specialized center in education and human resource development, so it is necessary to create conditions for students to become regular learners, lifelong learners by developing their needs and motivations for learning and obtaining knowledge and skills for self-study. Developing awareness needs and forming self-study skills for students in universities is one of the most important conditions to contribute to building and deploying the "learning citizen" model. On the basis of practical assessment, consultation with experts, and study of guiding documents of the State, this study focuses on clarifying the need to develop cognitive needs for students in the field of education in universities to deploy and build a model of "learning citizen" in the context of globalization, that is formation, development of motivation, learning needs, individual cognitive needs, skills self-study, etc. From there, recommend solutions to develop this model in the future.
P94. A Transdiagnostic Study of Morphometric Similarity Networks in Delusions in Dementia

Delusions in dementia of the Alzheimer’s type (DAT) and behavioral variant frontotemporal dementia (bvFTD) are distressing and lead to psychiatric misdiagnoses, and antipsychotic use has been found to predict mortality in dementia. We identified structural brain networks associated with delusions across bvFTD and DAT.
Non-popular Items for Accurate &amp; Diverse Linear Auto-Encoding Recommenders

High-dimensional linear models are some of the best performing collaborative filtering models today. They learn full-rank item embeddings by inverting the Gram matrix calculated from the input user-item matrix. The entries of this Gram matrix are item co-occurrence counts which are unbounded. As a result, the Gram matrix is dominated by larger co-occurrence counts of popular items. In this paper, we propose to alleviate this issue by incorporating cosine similarity with the co-occurrence counts. We show that this increases the recommender diversity and more non-popular items are recommended. We also show that this increase in diversity correlates with an increase in accuracy signifying that the newly recommended non-popular items are relevant. Finally, we also present a more efficient procedure to obtain the parameters of linear auto-encoding recommender and show that it reduces the running time by at least half on the three standard publicly available datasets used for this line of research.
COVID-19 Contact Tracing Analysis with Bluetooth Technology Using Raspberry Pis

Abstract Contact tracing, a method for detecting and preventing the spread of a disease, can become more efficient by becoming automated, rather than being done manually. Bluetooth based contact tracing is a potential method for creating an automated method for contact tracing. However, Bluetooth signals cannot always predict something with complete accuracy due to many subtle obstructions. Received signal strength indicator (RSSI), a value produced when Bluetooth devices send and receive signals, generated from Raspberry Pis can predict the distance between the two devices. By running many experiments with variations of obstructions, I was able to successfully create models to correlate RSSI values and distance with a potential success rate of 91.973%. For my research, a success is determined to be anything but a false negative. Despite the limitations when conducting my research, the inaccuracies of my results prove Bluetooth based contact tracing to not be a reliable method for determining people who have been in contact with an index case in the real world.
A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots

Duckiebots are low-cost mobile robots that are widely used in the fields of research and education. Although there are existing self-driving algorithms for the Duckietown platform, they are either too complex or perform too poorly to navigate a multi-lane track. Moreover, it is essential to give memory and computational resources to a Duckiebot so it can perform additional tasks such as out-of-distribution input detection. In order to satisfy these constraints, we built a low-cost autonomous driving algorithm capable of driving on a two-lane track. The algorithm uses traditional computer vision techniques to identify the central lane on the track and obtain the relevant steering angle. The steering is then controlled by a PID controller that smoothens the movement of the Duckiebot. The performance of the algorithm was compared to that of the NeurIPS 2018 AI Driving Olympics (AIDO) finalists, and it outperformed all but one finalists. The two main contributions of our algorithm are its low computational requirements and very quick set-up, with ongoing efforts to make it more reliable.
Study on optimal method of steel structure based on fuzzy level analysis

Steel structure hoisting construction is a common construction type of substation construction project. Influenced by various factors such as narrow construction site, high altitude operation, high edge operation, and multi-work cross operation, to improve the safety and construction efficiency of steel structure hoisting construction of the substation has become one of the projects facing infrastructure projects of power grid enterprises. Based on the construction scheme and the main procedure, the optimal evaluation method based on fuzzy level analysis is constructed and the effectiveness of the model is verified with typical cases.
Mineralogical characteristics and SEM-EDS investigation of daughter minerals of fluid inclusions at the Dongbulage Mo-Pb-Zn deposit, Inner Mongolia

The Dongbulage Mo-Pb-Zn deposit is located in Xi Ujimqin county, Inner Mongolia. In this paper, on the basis of microscopic investigation of the mineral assemblages and fluid inclusion petrology, Scanning Electron Microscopy and X-ray Energy Dispersive Spectrum (SEM-EDS) were employed in identification of the ore minerals and the solid phases of fluid inclusions. The results showed that silver minerals exist in the form of natural silver which mainly coexists with silicate minerals and apatite. The daughter minerals of fluid inclusions mainly include halite, sylvite, calcite and fine columnar ferric salt. With the progress of mineralization, the ore-forming fluid gradually changes from oxidation to reduction. Phase separation (or boiling) occurs before the hydrothermal solution is captured. This process will inevitably lead to the increase of salinity of ore-forming hydrothermal solution.
On Galois hulls of linear codes and new entanglement-assisted quantum error-correcting codes

The Galois hull of a linear code is the intersection of itself and its Galois dual code, which has aroused the interest of researchers in these years. In this paper, we study Galois hulls of linear codes. Firstly, the symmetry of the dimensions of Galois hulls of linear codes is found. Some new necessary and sufficient conditions for linear codes being Galois self-orthogonal codes, Galois self-dual codes, and Galois linear complementary dual codes are characterized. Then, we propose explicit methods to construct Galois self-orthogonal codes of larger length from given Galois self-orthogonal codes. As an application, linear codes of larger length with Galois hulls of arbitrary dimensions are further derived. Focusing on the Hermitian inner product, two new classes of Hermitian self-orthogonal maximum distance separable (MDS) codes are also constructed. Finally, applying all the results to the construction of entanglement-assisted quantum error-correcting codes (EAQECCs), many new $q$-ary or $\sqrt{q}$-ary EAQECCs and MDS EAQECCs with rates greater than or equal to $\frac{1}{2}$ and positive net rates can be obtained. Moreover, the minimum distance of many $\sqrt{q}$-ary MDS EAQECCs of length $n>\sqrt{q}+1$ is greater than or equal to $\lceil \frac{\sqrt{q}}{2} \rceil$.
Utilization of Online Learning Platforms to Improve Elementary School Teachers’ICT Competence

To support Learning From Home (BDR), elementary school teachers in Kapanewon Kokap, Yogyakarta, have low ICT skills. This service seeks to improve the ICT competence of elementary school teachers in Kapanewon Kokap. This service activity is carried out in an action research design that adopts a workshop procedure. This activity is divided into three stages. In the first and second stages, classical discussions (supervision) were carried out which were guided by professionals. The topics of discussion are various online learning platforms that can be used by participants. In the third stage, participants practice developing and utilizing online learning platforms, then presenting them to supervisors and other participants. The results of this service activity indicate a significant increase in ICT competence, both at the level of knowledge acquisition, knowledge deepening, and knowledge creation. This can be seen from the increase in the score of the post-test results. This increase can facilitate the implementation of their obligations to provide appropriate education and teaching according to the characteristics of today's children.&#x0D; Abstrak&#x0D; Untuk mendukung Belajar Dari Rumah (BDR), guru SD di Kapanewon Kokap, Yogyakarta, memiliki keterampilan TIK yang rendah. Pengabdian ini berusaha meningkatkan kompetensi TIK guru SD di Kapanewon Kokap. Kegiatan pengabdian ini dilaksanakan dalam desain penelitian tindakan yang mengadopsi prosedur workshop. Kegiatan ini terbagi ke dalam tiga tahap. Pada tahap pertama dan kedua, dilakukan diskusi klasikal (supervisi) yang dibimbing oleh profesional. Topik diskusi adalah berbagai platform pembelajaran online yang dapat digunakan oleh peserta. Pada tahap ketiga, peserta berlatih mengembangkan dan memanfaatkan platform pembelajaran online, kemudian mempresentasikannya kepada supervisi dan peserta lainnya. Hasil kegiatan pengabdian ini mengindikasikan adanya peningkatan kompetensi TIK yang signifikan, baik pada tingkat perolehan pengetahuan, pendalaman pengetahuan, maupun penciptaan pengetahuan. Hal ini terlihat dari peningkatan skor hasil post-test. Peningkatan tersebut dapat mempermudah pelaksanaan kewajiban mereka untuk memberikan pendidikan dan pengajaran yang tepat sesuai dengan karakteristik anak-anak saat ini.