Reviewerly/exHarmony huggingface dataset:
Distance between compact set and point in constructive metric space

In metric space, for any fixed point outside the compact set, we can always find a point within the compact set that minimizes the distance between two points. Whether it still has the same property in the constructive metric space is the focus of this paper. Based on the constructive real number and constructive metric space, this paper discusses this property in constructive metric space, and we explore the distance function between a point and a compact set in the constructive metric space. Then we constructed an example of constructive metric space 𝑋 such that no algorithm can always choose one point 𝑐 of compact set 𝐶 𝑖𝑛 𝑋 which is the closest point to a given point 𝑥 &isin; 𝑋. Eventually, the conclusion of constructive metric space is different from that in metric space, which shows the difference between metric space and constructive metric space.
User-Experience and Usability Review of a Smartphone Application

In the last decade, information technology products and environments have been changing at a rapid pace. Businesses and enterprises have highlighted the need to focus on the users’ needs when developing a product; in consequence, decision-makers need to adapt faster to keep up with the rapidly changing market and technological environment. Agile Software Engineering and Lean Startup approaches were created to address this situation. Usability and user-experience (UX) were the last to jump on the train, demanding a change in how they were assessed. The aim of this chapter is to illustrate key aspects of the iterative process of adapting user test design (procedure and assessment tools) in a software as a service agile startup. It describes the adjustments made to the test design depending on the aim of each iteration. The results were translated into actionable steps to change the product. This chapter provides an overview of how companies can implement human-centered design methodologies such as UX design and usability, and the right way is to start small and build up from there. In the end, building a product or service is a learning process; every test revealed not only answers but also new questions and features.
Implementation of Machine Learning using Fastai for Image Classification on the Automatic Waste Sorter Prototype

Indonesia is one of the countries that contribute to waste in the world.Waste management is one way to reduce waste generated.Sensing the type of waste with a camera using computer vision is one method to take waste images.Using this method can create an automatic waste sorting system.The scattered waste is mixed of many types, including cardboard, glass, metal, paper, and plastic.This research uses the machine learning Convolutional Neural Network (CNN) Model to classify waste.The prototype uses a notebook to process classification and sends the classification to Arduino with Firmata protocol.The error rate value obtained from the train was 0.096708.while the accuracy value of the prototype is 84%. in this research, to be able to apply waste separation technology automatically to the waste, it can use fastbook when using a server computer and Arduino to control the device prototype.
Design and analysis of a special digital signature scheme

In this work we analyze the BG14 digital signature, and show that its security-proof is incomplete in the sense that a necessary security check is missing in its sign algorithm. Moreover, we offer an alternative approach for the phase 2 security-proof of the BG14 scheme. These two contributions are summarized in our digital signature scheme <i>&Pi;</i>.
Geochemical features of the impact of the Gayskoye copper pyrite deposit on the environment

Abstract The eastern part of the Orenburg region is the largest center of the mining and processing industry of non-ferrous metals in the Ural region. The most significant are copper pyrite deposits containing ores of various chemical compositions. As a result of the study, it was found that in the area of development of the Gayskoye deposit, geochemical halos are developed around ore bodies in the rocks of the Paleozoic, Meso-Cenozoic and weathering crusts. Landscape-geochemical conditions contribute to the wide migration of ore elements and their accumulation in various environments - soils, groundwater, vegetation. Elevated contents of all ore elements are observed, many of them exceed the MAC, Pb, Cu, Zn, As should be attributed to ecologically hazardous elements.
Predictive study of diabetic readmission with coupled A-RES and integrated learning algorithm

Repeated hospitalizations have brought a heavy economic burden to the society. Exploring the establishment of a dynamic prediction model for readmission is helpful for early intervention. Based on the machine learning algorithm, a dynamic prediction model of diabetes readmission combined with the A-RES algorithm is proposed. SMOTE oversampling for data imbalance use, encoding processing and One-Hot encoding processing for medical texts are used to train prediction models of random forest, GBDT, and XGBOOST, and the A-RES algorithm is used to combine them for training. The learning results show that the GBDT+RES model has the best performance, with an AUC score of 0.973 points, which is an increase of 0.12 points compared with the original model. The final prediction models all have good model performance.
Pemanfaatan Big Data dalam Monitoring Pola Aktivitas Aviasi di Indonesia

Abstract: Covid-19 which entered Indonesia in December 2019 has a significant impact on the aviation industry. According to BPS data for 2020, the aviation industry's contribution to Indonesia's GDP decreased from 1.21% to 0.28% in the second quarter of 2020. To overcome this setback, comprehensive monitoring by policy makers is needed. The use of big data in monitoring aviation industry activities can be an option. This study aims to analyze aviation activities using big data approach for monitoring basis. The data was collected by using web scraping method on one of the global aviation websites to obtain flight status data at 108 airports in Indonesia on April 2020 until June 2021. Other data used are google mobility index data, GDP data, and TPK. The analysis method used are descriptive analysis, correlation analysis and machine learning based time series modelling with ARNN, single layer ANN and MLP. The results show that the policy of restricting mobility has a significant effect on the productivity of aviation industry. Machine learning modeling shows that the MLP model is the best model for forecasting international aviation activity. In addition, it was found that the aviation industry has a strong correlation with the economy and tourism sector in Indonesia.
Optimization of RFM for automated breast cancer detection

Recent years have seen an upsurge in the acceptance of illness diagnosis and prediction utilizing ML algorithms. A ML model can be employed in the diagnosis of breast cancer illness. In this research, an effective breast cancer prediction model with grid search approach is provided. Using the random forest approach, grid search is used to find the best n-estimator, which may provide the highest possible accuracy for predicting breast cancer. The accuracy of the suggested model can then be utilised to contrast its effectiveness to that of a standard RFM. The experimental result analysis demonstrates that the optimized model has 97.07 percent accuracy whereas the regular random forest technique has an accuracy of 94.73 percent in breast cancer detection.
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required for edge devices due to their limited memory. To alleviate such an issue, we propose a novel diversity-aware incremental learning method named Rainbow Keywords (RK). Specifically, the proposed RK approach introduces a diversity-aware sampler to select a diverse set from historical and incoming keywords by calculating classification uncertainty. As a result, the RK approach can incrementally learn new tasks without forgetting prior knowledge. Besides, the RK approach also proposes data augmentation and knowledge distillation loss function for efficient memory management on the edge device. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline on Google Speech Command dataset with less required memory. The scripts are available on GitHub.
Orienting Data Services in the Library

This chapter describes the evolution of a department in a large academic library dedicated to the provision of research support for data and related digital scholarship services. Topics covered include an overview of organizational changes and priorities in the library, alignment of services to available infrastructure, and approaches to outreach and instruction that have been successful in meeting ever-changing researcher needs. The authors describe staffing and staff training, data repositories and related systems, and common topics covered in consultations and workshops over the past decade with particular focus on development and delivery of a Carpentries-based instruction program. The chapter concludes with reflections on considerations for the future and broad recommendations for the ways libraries can continue to meet emerging data science needs in a research university context.
Reference Path Generation for Autonomous Driving in Off-road Environment

This paper proposes a reference path generation method for Autonomous Vehicles(AVs) in the off-road environment. Since obtaining sufficient and reliable prior information in an off-road environment is difficult, we adopt the Astar strategy to complete global routing based on the satellite topology map. Then planning result is corrected based on vehicle localization to obtain the initial reference path. Because the road geometry changes according to the road environment, we will use the centerline of the drivable area extracted with Probabilistic Neural Networks(PNN) to generate the final reference path. While the drivable area is invalid, the reference path planner is applied based on the initial reference path to form the final reference path. In the real off-road scenarios, the proposed method is tested on our experimental vehicle. Experimental results demonstrated that the proposed algorithm could generate reliable reference paths effectively and improve the performance and robustness of local trajectory planning.
Study on different substations based on equipment operation safety risk

Under the new situation, with the continuous advancement of the reform of the power system, the operation and maintenance investment management of substations has gradually developed in the direction of "intelligence and intensive". Therefore, strengthening the scientific nature of substation operation and maintenance management and improving the lean nature of substation operation and maintenance investment has become one of the development goals of cost investment management for power grid companies. Based on the current status of substation operation and maintenance investment management, this paper constructs a comprehensive evaluation index system and evaluation method for the operation safety risk of substation equipment, and combines the comprehensive evaluation scores of different substations to determine the annual operation and maintenance investment allocation quota for each substation, thereby improving substation operation and maintenance The scientific nature of input allocation.
[Integrating dynamic elements to static biochemistry-the practice of unitized "micro-teaching"].

As the content of static biochemistry is boring and traditional cramming education fails to draw the attention of students, we divided the course into small units and then made full use of "micro-teaching" resources such as multimedia slides, pictures, objects, experimental demonstrations and animations of the biochemical principles. The method created a "micro-teaching" environment, which combined boring static knowledge with vivid dynamic elements, thus producing a strong sensory impact on students, highlighting the knowledge in their mind, prompting students' participation, and greatly improving the teaching effects. This article introduces the goals and development process of this teaching method, and takes "Protein Denaturation and Renaturation" as an example to demonstrate the design and implementation process of the "micro-teaching", which seems to be of great significance to the improvement of the teaching quality of biochemistry course and serves as a reference for reform in other courses.
‘You Only Look Once’ Application for Autonomous Driving Vehicles &amp; Cricket Spidercams using Convolutional Neural Network in Deep Learning

Road safety is a prime concern in this era of high speed and automated driving vehicles. Lot of lives are lost or injured every day due to road accidents. Just understanding where the roads are is not adequate for an autonomous vehicle, obstacles like other vehicles and even less impact-resistant pedestrians and cyclists should be identified and avoided. Moreover, a technology proposed should also be capable to augment itself to provide other applications in the related fields. The proposed method here recognizes and report to the system about the objects such as cars, pedestrians, animals, etc. Once the object is identified, the next time vehicle approaches the similar object, it notifies the driver. And it also tells the system whether the object is moving towards or away from our vehicle. Augmenting this algorithm in applications like that of self-driven vehicle or automobiles/devices using Artificial Intelligence for the blind can be made for better safety. The system developed will be subjected to trials in the real life and correlated with an experimental setup.
Pengembangan Media Pembelajaran Interaktif Berbasis Articulate Storyline pada Materi Komunikasi Telepon dalam Bahasa Inggris di SMK IPIEMS Surabaya

This study aims to analyze the development process as well as the response of students to interactive media in the field of education based on Articulate Storyline on the material of Telephone Communication in English. This research method uses research and development with the ADDIE model. The subject of the research trial was a group of 20 students. X OTKP 1 SMK IPIEMS Surabaya. The data collection instrument used expert validation sheets and student response questionnaires. The data analysis technique of this research used descriptive analysis. The results of this study indicate that: 1) the process of developing interactive media for teaching and learning based on Articulate Storyline is ongoing with Telephone Communication in English class X OTKP 1 SMK IPIEMS Surabaya carried out through four steps, namely Analysis, Design, Development, and Implementation; 2) learning media is categorized as feasible based on the average validation value obtained from the three validators, which is 97%; 3) the results of student responses that obtained an average value of 92%. Therefore, it can be concluded that the interactive learning media based on Articulate Storyline on Telephone Communication in English is very feasible to be used for learning media facilities at the learning and teaching stages at SMK IPIEMS Surabaya and getting a positive response in the "very good" category by SMK IPIEMS students. Surabaya.
Review on Cloud Security and Challenges on Higher Education

Many new developments are often plagued with security concerns. Although emerging innovations have all the potential to change systems and people's lives, security issues hinder them adoption and usage. The transformations of data management brought by cloud technologies have helped both individuals and companies. While the cloud allows data storage, connectivity, and recovery stability, it is not immune to the security threats that other new technologies pose. Concerns have been presented over the protection and privacy of cloud users' data. Data leakage, data manipulation, condemnation, a Lack of Services assault, impersonating personalities, also unauthorised access to data are all examples of activities which can appear while data is outsourced to a cloud. Individuals and organisations will orchestrate these attacks, which will jeopardise the reliability and security of cloud networks. The vast majority of colleges and universities are digitising their records, which are then stored in the cloud and handled by a third party. Despite the sacred importance of educational information, the study explores the impact of cloud security problems on schooling. According to the findings, security is crucial for the secure migration and application of cloud technology in the educational sector. It also shows that the growing security threats associated with cloud technology can prevent many education institutions from using cloud services.&#x0D; &#x0D; Keywords: Cloud technology, Cloud Security, Educational, Information leakage, Privacy
Automated Quantification of Ki-67 on Gastric Epithelial Tissue based on Cell Nuclei Area Ratio

The objective was to develop an automated algorithm for the estimation of a protein (Ki-67) index based on cell nuclei area ratio of gastric epithelial tissue cells; for this purpose, digital histopathology images were used. An expert manually annotated each region of interest of the images. A proportion of Ki-67 positive and negative cells within that region was used to obtain the color distribution of the corresponding pixels. The histogram of each color distribution was modeled as a Gaussian and, later, thresholded for segmentation and classification. Finally, the Ki-67 index was estimated as the ratio between the segmented positive area of the nuclei divided by the total area of the positive and negative nuclei. The automated method has a strong correlation of 0.725 and a root mean square error of 0.293 when compared to the manual method, which gives certainty that the automated method can be used to analyze the proliferation rate. Furthermore, compared to manual classification, the presented method automatically classifies every image in the same Ki-67 category: low, intermediate, and high. Despite the small sample size, the utility of the presented method was demonstrated. However, the low number of scored images did not allow for thoroughly sampling the ranges of pixel values and intensities observed by pathologists, which will be addressed in future work.
“Good Vibrations” Teil I. Vibration zur Behandlung von Patellartendinopathie

We provide a proofs-as-concurrent-programs interpretation for a large class of intermediate logics that can be formalized by cut-free hypersequent calculi. Obtained by adding classical disjunctive tautologies to intuitionistic logic, these logics are used to type concurrent λ-calculi by Curry–Howard correspondence; each of the calculi features a specific communication mechanism, enhanced expressive power when compared to the λ-calculus, and implements forms of code mobility. We thus confirm Avron's 1991 thesis that intermediate logics formalizable by hypersequent calculi can serve as basis for concurrent λ-calculi.
CCIP: combined CCLM and intra prediction

In this paper, we propose a new coding method, called combined CCLM and intra prediction (CCIP) to improve the prediction efficiency of the cross component linear model (CCLM) in versatile video coding (VVC) standard for chroma. While the CCLM technique in VVC can use the linear correlation between the current chroma block and its co-located reconstructed luma region, it cannot take into account the correlation existing between the current chroma block and its adjacent chroma blocks in prediction. The proposed CCIP overcomes this shortcoming by combining two predictors, respectively, generated by conventional CCLM models and by intra prediction using its spatial chroma reference sample. Our experimental results show BDBR of 0.06% in Y component, -0.60% in Cb, and -0.54% in Cr in 4:2:0 color format, and -0.09% in Y component, -0.33% in Cb, -0.42% in Cr in 4:4:4 color format.
Smart Attendance System Using AR Marker

Abstract: In the current scenario smartphones play a vital role in our daily life, In fact they are a part of our lives. Smartphones can now handle the majority of problems fast and easily. Every person's life has been made simpler and easier as a result of it.Basically this paper focuses on the problem which is faced by the faculties and students during the process of attendance and there are more chances of proxies in the traditional method (using pen and papers). The paper proposed a system that will use AR Marker technology and handle the problem for recording the attendance of the students in school and colleges. The suggested system consists of two applications: one for creating the AR Marker by entering class information via faculty, and another for scanning the QR code via students. The teacher will generate the QR code and present it to the students using a projector, after which the students will simply open the second app and scan the QR code to get their attendance. The Smart Attendance System is cost effective because it does not rely on biometric devices which are expensive. The paper explains how the system authenticates students' identities in order to prevent fraudulent registrations. By changing the QR code every five to ten seconds and just for five minutes, the system is able to reduce the proxy level. If any student fails to show up for class, the professor may display the QR code again. The system is in charge of managing and evaluating all students' attendance. In addition, the system will save the attendance data in XLS and CSV files for future use. Keyword: Smart Attendance System, AR Marker, Attendance System using QR Code, Software as a Service .
A Blockchain Security Management Based on Rehashing Shift Code Rail Encryption Using Circular Shift Round Random Padding Key for Decentralized Cloud Environment

Blockchain is currently being developed as the most promising security policy and dynamic technology for cloud construction security. Data transactions are confirmed with the database systems, which are distributed in the store as a Blockchain method. But most of the aggregates in cloud nodes fail in key security aspects due to attacks in nature and trust in the shared names of real related distributions. Blockchains use planned data structures that are considered highly informative due to illegal intervention. In this chapter, we will provide Blockchain security that can be implemented by rehashing key security policies. To propose a rehashing shift code rail encryption (RSCRE) using circular shift round random padding key for decentralized cloud environment (CSR2PK). To facilitate site-centric and secure decisions, we propose a rail block-based Blockchain voting protocol for voting aggregation peer requests. Blockchain networks without the need for third-party trust. Privacy verifies from potential voting aggregators to authenticate the key at the peer end to enhance the security. Our protocol improves privacy by verifying the peer voting to aggregate the data owner request policy. Hyperledger fabric is provided by standard security and a practical description of our protocol to improve the security.