Reviewerly/exHarmony huggingface dataset:
Development of a Self-X Sensory Electronics for Anomaly Detection and its Conceptual Implementation on an XMR-based Angular Decoder Prototype

Efficient fault diagnosis, early anomaly detection, and prevention of equipment failures are crucial for reducing production costs and minimizing unplanned downtime.This paper focuses on developing a self-X system using artificial immune system algorithms to address the challenges of condition monitoring, quality control, and predictive maintenance in Industry 4.0.The proposed methodology incorporates self-monitoring, self-healing, and self-repairing capabilities into intelligent measuring systems equipped with a tunnel magnetoresistive (TMR) sensor-based angular decoder.In particular, the extension of the self-X hierarchy to the adaptive electronics level, with a focus on healing/adjustments before ADC's irreversible quantization loss is a key objective.The implemented self-X approach enables dynamic offset and gain compensation, improving angle accuracy.The experimental setup involves a reconfigurable analog front end with self-X properties (AFEX), a data acquisition unit, feature extraction, self-monitoring, and self-healing mechanisms.The results demonstrate the successful implementation of gain compensation using a fabricated current-feedback instrumentation amplifier.The experiments show the impact of signal amplitude drop on angle error calculation, with the maximum absolute error observed at 11 mm TMR sensor position.The self-X loop, including electronics, reduced the angle error to approximately 50 % by increasing the gain from 4 to 32.The results obtained from first experiment indicate a maximum absolute error of -6.552 °in angle computation, which is not yet competitive to
35th Himalayan-Karakoram-Tibet Workshop (HKT) “Geosciences for People’s Prosperity and Sustainable Development” November 02-04, 2022

This is the full volume of the Abstracts from the 35th Himalayan-Karakoram-Tibet Workshop (HKT) on “Geosciences for People’s Prosperity and Sustainable Development” November 02-04, 2022, Pokhara, Nepal.
TO THE 75TH ANNIVERSARY OF ACADEMICIAN OF THE RUSSIAN ACADEMY OF SCIENCES FEDOR VASILIEVICH GRECHNIKOV

The article is devoted to the life path and achievements of the Academician of the Russian Academy of Sciences Fedor Vasilievich Grechnikov
ICDAR 2023 Competition on Document UnderstanDing of Everything (DUDE)

This paper presents the results of the ICDAR 2023 competition on Document UnderstanDing of Everything. DUDE introduces a new dataset comprising 5 K visually-rich documents (VRDs) with 40 K questions with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. The competition was structured as a single task with a multi-phased evaluation protocol that assesses the few-shot capabilities of models by testing generalization to previously unseen questions and domains, a condition essential to business use cases prevailing in the field. A new and independent diagnostic test set is additionally constructed for fine-grained performance analysis. A thorough analysis of results from different participant methods is presented. Under the newly studied settings, current state-of-the-art models show a significant performance gap, even when improving visual evidence and handling multi-page documents. We conclude that the DUDE dataset proposed in this competition will be an essential, long-standing benchmark to further explore for achieving improved generalization and adaptation under low-resource fine-tuning, as desired in the real world.
ANALISIS LINGKUNGAN BELAJAR UNTUK MEDIA MENULIS BAGI SISWA

The research data source is the results of interviews with Indonesian teachers in Jabodetabek. From the results of the research above, it can be concluded that the learning environment affects students' writing skills. There are two learning environments, namely indoors and outdoors. The learning media used by the teacher in writing poetry are audiovisual, audio, hands-on, projects, puzzles, picture cards, and others
RANCANG BANGUN APLIKASI DATA BUKU DAN TOKO BUKU DENGAN METODE LOCATION-BASED SERVICE BERBASIS ANDROID

This system is one of the online bookstore system that sells various categories of books, whose products are sourced from several suppliers. Bookstores located in Lhokseumawe have difficulty in disseminating product information. This research focus to Designing Data Book Apps And Bookstores Based on Location Based Service Methods and Android has a problem formula for how to design and build book-logging apps and bookstores. The purpose of this research is to make the app as a product promotion medium for booksellers and to provide nearby bookstore information for people from out of town by using. This research uses location based service methods. The data sources used are primary and secondary data from Google Maps. Based on the testing conducted, it is obtained the conclusion that users of this system do not have to have a physical store to sell books, and for bookstore seekers must enable GPS to locate the starting point to the nearest bookstore location point.Keywords : Bookstores, Location Based Service
Evaluasi Data Curah Hujan Satelit TRMM Dan GPM Terhadap Data Curah Hujan Observasi Di Kalimantan Tengah

Rainfall is an indispensable component of hydrological analysis. However, the observational rainfall data has problems such as incomplete and shorter data. Rainfall measurements using high resolution and wide area coverage satellites provide a potential alternative for various hydrological analyses. But the measurement of rainfall using satellites is not necessarily accurate due to the difference in resolution of the observational rainfall data, especially in Central Kalimantan Province. This study aims to determine the accuracy of satellite rainfall data based on observational rain data and evaluate it. Evaluation is done by looking for the relationship between the two-rainfall data using a regression model. The results showed that the TRMM total grid rainfall data (R2 = 0.5233) had a higher correlation to the observational rainfall data compared to the GPM rainfall data (R2 = 0.3909). Furthermore, a discharge analysis was carried out in one of the watersheds in Central Kalimantan, namely the Kahayan watershed using the Nakayasu Hydrograph Synthesis Unit method with a certain return period with satellite rainfall data input which has a fairly good accuracy based on the results of the analysis. The estimated discharge with return periods of 2, 5, 10, 25, and 50 years for TRMM rainfall data before correction are 1976.74m3/s, 2165.66 m3/s, 2239.19 m3/s, 2313.63 m3/s, 2375.69 m3/s respectively, and for the estimated discharge after correction are 2549.35 m3/s, 2670.59 m3/s, 2736.42 m3/s, 2801.37 m3/s, 2854.82 m3/s.
On-Chain Video Copy Detection Based on Swin-Transformer and Deep Hashing

Abstract In recent years, short videos are spreading faster and become higher quality due to edge-cloud technology. People receive information gradually from graphic to video. At the same time as the number of videos spread rapidly, infringing videos are also flooding the Internet. The wild spread of infringing videos on the Internet has brought serious losses to video websites and original authors. Although video copy detection can solve such problems, the detection results are easy to be tampered with, and the detection results are hardly convincing. Based on this, this paper proposes an open, transparent and verifiable video copy detection method, which uses blockchain technology to ensure the transparency and openness of the results. In addition, this method adopts the combination of on-chain and off-chain methods to automatically perform copyright detection by invoking smart contracts on the chain. This mechanism can securely and immutably store video feature values on the blockchain, ensuring the originality of copyrighted works and the ability to verify detection results. Swin-Transformer and deep hashing are used to obtain video features off the blockchain, which can efficiently match the similarity of existing videos. The method of block comparison can greatly shorten the comparison time, which is 1/50 of the traditional comparison time. Experimental results show that this method is very effective in retrieving similar images and detecting the similarity between original and pirated videos.
Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment

&lt;p&gt;The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.&lt;/p&gt;
Value Universitas dan Perilaku Cyber Cheating: Analisis Teori Fraud Diamond

The purpose of this study is to examine the factors that drive accounting students to commit academic fraud based on the fraud diamond theory (pressure, opportunity, rationalization and ability) and technology abuse by considering university values. This study used purposive sampling as a sampling method using a questionnaire. The sample of this research was 170 Bachelor of Accounting students at Christian Private University, Yogyakarta. The data analysis method was processed using the Structural Equation Model (SEM). Specifically, the SEM method chosen is Partial Least Square (PLS) because it can be used to analyze data with a small number of samples (Hair et al, 2014). The results of this study succeeded in proving that pressure, opportunity, rationalization, ability and misuse of technology have a significant positive effect on academic cheating. This revealed that students who study at universities with Christian values still separate spiritual interests and academic achievements.
Investigation of the Turkish university rankings using InterCriteria Analysis

In the current investigation Turkish university rankings are analyzed. The dataset for overall ranking of universities for 2021-2022 is used. The information is downloaded from the University Ranking by Academic Performance website. Dependencies and independencies between Turkish universities are analyzed. The relationship between university rankings indicators are investigated.
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations

Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification.
Learning Moore-Penrose based residuals for robust non-blind image deconvolution

This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.
Accompaniment How to Quickly Read The Bold Book (CBKG) Phase II With Qawa'id And Tarjamah Methods for Students of The Mambaul Ihsan Islamic Boarding School

The purpose of community service activities in this form is to provide advanced basic knowledge and skills regarding procedures for reading bare books using the Qawa'id and Tarjamah methods and to foster a passion for reading bare books through CBKG. The method used uses the method in the workings of PAR (Participatory Action Research). The findings and results of this activity show that there is an increase in basic knowledge and skills greater than stage I regarding the procedure for reading bare books using the Qawa'id and Tarjamah methods on the book Fathul Mu'in and it turns out that it can also foster a passion for reading bare books. Tujuan kegiatan pengabdian masyarakat dalam bentuk ini adalah ingin memberikan pengetahuan dan keterampilan dasar lanjutan mengenai tata cara baca kitab gundul dengan metode Qawa’id dan Tarjamah dan Ingin menumbuhkan semangat gemar membaca kitab gundul melalui CBKG.Metode yang digunakan menggunakan metode dalam cara kerja PAR (Participatory Action Research). Adapun temuan dan hasil dari kegiatan ini adalah nampak adanya peningkatan pengetahuan dan keterampilan dasar lebih besar dari tahap I mengenai tata cara baca kitab gundul dengan metode Qawa’id dan Tarjamah pada kitab Fathul Mu’in dan ternyata juga dapat menumbuhkan semangat gemar membaca kitab gundul.
Incremental Learning and Ambiguity Rejection for Document Classification

Along with the innovation and development of society, millions of documents are generated daily, and new types of documents related to new activities and services appear regularly. In the workflow for processing these documents, the first step is to classify the received documents to assign them to the relevant departments or staff. Therefore, two major problems arise: 1) the document classification algorithm is required to not only learn new categories efficiently but also have low error rate on them; 2) document classification needs to have the ability to detect when new classes appear. To address the first problem, we propose a class incremental learning method combined with ambiguity rejection to reduce the error rate of document classification. In this method, the ambiguity rejection module uses the classifier’s probabilities and statistical analysis to optimize per-class thresholds for filtering out (rejecting) uncertain results, thereby increasing the accuracy of non-filtered documents. Our method is thoroughly evaluated on a public business document dataset and on our private administrative document dataset including 36 categories of more than 23 thousand images. On our private dataset, the method increases the classification accuracy of 0.82%, from 98.28% to 99.10%, while the rejection rate is only 3.42%. Additionally, with incremental learning scenario, it can achieve error and rejection rates of 1% and 8.98%, respectively. This result has great significance, can also be applied to similar document datasets, and especially has great potential for real-world applications.
Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Stream sediment samples play an important role in identifying potential areas of metallic and non-metallic mineralization in mineral exploration studies. The relationship of geochemical elements with each other shows how the elements are distributed in the area. Also, by identifying related elements, sampling and targeted chemical analysis can be used in the next stages of exploration. The purpose of this study is to investigate the elements related to the copper element in the Siojan prospecting area, which is located in South-Khorasan province and 30 km northwest of Birjand city of Iran. In Siojan area, 120 stream sediment samples of a 60 square kilometer area were collected to detect geochemical anomalies and were consequently analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for 45 elements. Preliminary geological studies showed that the studied area has copper mineralization potential, and therefore, copper was selected as the target element in this study. Copper trace elements were identified in the area and the results were used to identify copper mineralized anomalies. For the elemental analysis data, methods of Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and K-Means Clustering were performed to identify the relevant elements and relationships among them. Statistical analysis of the concentration of geochemical elements in the region revealed that copper and cobalt elements were identified as two elements of the same family in terms of geochemical genetics. The average value for copper and cobalt elements in the analyzed samples was 27.2 ppm and 15.5 ppm, respectively. Finally, the relationship between copper and cobalt elements was modeled as an equation using the K-Means Clustering algorithm.
Development of Action-Intention Indicator for Sidewalk Vehicles

Many small ground vehicles that travel on sidewalks, such as electric wheelchairs, are equipped with a differential-wheeled mechanism to turn precisely on the spot and accurately control their trajectory. Conventional ground vehicles operating on public roads have action indicator devices such as blinkers and brake lights, which are effective in preventing collisions during operations. However, similar blinkers may not be suitable for sidewalk navigation because the differential-wheeled mechanism allows a neutral turn without considering the surroundings. In this study, we developed a new action-intention indicator for future differential-wheeled mobile robots that employs RGB light-emitting diode strip lights to express various colors and complex blinking patterns. Sensitivity evaluation experiments were conducted with approximately 50 subjects to determine the optimal colors and blinking patterns for a differential-wheeled mobile robot. Additionally, reaction-time acquisition experiments were conducted to confirm the effectiveness of the proposed intention-display device for mobile robots. Finally, we evaluated whether the determined blinking patterns exhibited appropriate power consumption.
Innovation in the Three-Step Training pattern in Electronic Information Majors from the Perspective of the “Reform of Teachers, Teaching materials, and Teaching Methods”

” The Reform of Teachers, Teaching materials, and Teaching methods” is a key approach to solve the problems of insufficient educational capacity and unsustainable development of major group resources. It provides strong technical and talent support for the upgradation of local major industries and high-quality economic development. It has become the mission of polytechnic colleges in the times. Zhongshan Polytechnic focuses on technology integration and application innovation of service enterprises, especially small and medium-sized micro enterprises. Centering on "independent learning, collaborative learning, and situational learning", it innovates the school enterprise cooperation mode, creates three major industry education integration support platforms of "innovation and cultivation center, studio, and research institute", and explores and constructs “three major categories of curriculum systems” driven by R&amp;D innovative products that support the cultivation of three core professional abilities for talents in the electronic information industry. A competency oriented technical and skilled talent cultivation system has been established, forming a talent cultivation model of "integration of industry and education in three-step progression".
Fourth Industrial Revolution and Business Education Programme in Nigeria: Challenges and Opportunities

The fourth Industrial Revolution (4IR) is all about changing technologies and trends such as the Internet of Things (IoT) and artificial intelligence (AI) that will have broad and deep impact on all aspects of life such as education, health and business. The purpose of this paper is to determine the challenges and opportunities of 4IR for humanity in general and Business Education Programme in Nigeria in particular based on a desktop study. The concept of Industry 4.0 and historical development of Industrial Revolutions (IRs), emerging challenges brought by the 4IR with its opportunities were briefly analyzed. The paper equally examined Business Education programme in Nigeria with myriads of problems undermining effective training of students to meet the challenges of global technological age. For Business Education Programme in Nigeria to meet the challenges posed by the 4IR, the paper recommends among others; the need for curriculum review to meet the 21 st Century skills need of the industry, adequate funding of the programme in the area of ICT adoption, training and retraining of business teacher educators, and collaboration between academia and the industrial sector.
Effect of Sequencing of Items and Test Anxiety on Students’ Academic Performance

The sequencing of items and test anxiety on students’ performance on multiple-choice tests was investigated. Data was collected from 105 students from the College of Education for the study. A quasi-experimental design was used and 50-test item questions were used for data collection. One-way repeated measures analysis of variance, independent t-test, and Two-way ANOVA were used. The results show that there was an effect of item sequencing on student performance. Moreover, test anxiety affected student performance. Furthermore, the result found that there was an interaction between the sequencing of items and test anxiety on student performance. The study, therefore, recommended that tutors in Colleges of Education should use Easy to Hard item order. The study further recommended that tutors should encourage students to develop appropriate learning habits that will minimize their anxiety when taking tests. Finally, the study recommended that Colleges should organize seminars for tutors on how to manage students’ anxiety. Scholars Middle East Publishers